{"topic":"ai-medicine","sort":"top","limit":25,"offset":0,"count":25,"papers":[{"id":"doi:10.1146/annurev-bioeng-071516-044442","kind":"review","title":"Deep Learning in Medical Image Analysis","authors":["Dinggang G. Shen","Guorong Wu A","Heung‐Il Suk"],"author_count":3,"year":2017,"published":"2017-03-16","venue":"Annual Review of Biomedical Engineering","cited_by":4943,"doi":"10.1146/annurev-bioeng-071516-044442","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1146/annurev-bioeng-071516-044442","archive_url":"https://space-kitty.com/papers/doi:10.1146/annurev-bioeng-071516-044442","updated":null,"pmid":"28301734","pmcid":null,"openalex_id":"W2533800772","pdf_url":"https://www.annualreviews.org/doi/pdf/10.1146/annurev-bioeng-071516-044442","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Medical Image Segmentation Techniques"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1016/j.cell.2018.02.010","kind":"article","title":"Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning","authors":["Daniel Kermany","Michael H. Goldbaum","Wenjia Cai","Carolina C. S. Valentim","Huiying Liang","Sally L. Baxter","Alex S. McKeown","Ge Yang","Xiaokang Wu","Fangbing Yan","Justin Dong","Made K. Prasadha","Jacqueline Pei","Magdalene Yin Lin Ting","Jie Zhu","Christina Li","Sierra Hewett","Jason Dong","Ian Ziyar","Alexander Shi","Runze Zhang","Lianghong Zheng","Rui Hou","William T. Shi","Xin Fu"],"author_count":39,"year":2018,"published":"2018-02-01","venue":"Cell","cited_by":4828,"doi":"10.1016/j.cell.2018.02.010","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.cell.2018.02.010","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.cell.2018.02.010","updated":null,"pmid":"29474911","pmcid":null,"openalex_id":"W2788633781","pdf_url":"http://www.cell.com/article/S0092867418301545/pdf","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Retinal Imaging and Analysis"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1109/tmi.2018.2837502","kind":"article","title":"Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?","authors":["Olivier Bernard","Alain Lalande","Clément Zotti","Frédéric Cervenansky","Xin Yang","Pheng‐Ann Heng","Irem Cetin","Karim Lekadir","Óscar Cámara","Miguel Á. González Ballester","Gerard Sanromà","Sandy A. Napel","Steffen Erhard Petersen","Georgios Tziritas","Elias Grinias","Mahendra Khened","Varghese Alex Kollerathu","Ganapathy Krishnamurthi","Marc-Michel Rohé","Xavier Pennec","Maxime Sermesant B","Fabian Isensee","Paul F. Jäger","Klaus Hermann Maier-Hein","Peter M. Full"],"author_count":37,"year":2018,"published":"2018-05-17","venue":"IEEE Transactions on Medical Imaging","cited_by":2426,"doi":"10.1109/tmi.2018.2837502","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1109/tmi.2018.2837502","archive_url":"https://space-kitty.com/papers/doi:10.1109/tmi.2018.2837502","updated":null,"pmid":"29994302","pmcid":null,"openalex_id":"W2804047627","pdf_url":"https://qmro.qmul.ac.uk/xmlui/bitstream/123456789/42264/1/Bernard%20et%20al_Deep%20learning%20techniques%20automatic%20MRI_2018_Accepted.pdf","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Medical Image Segmentation Techniques"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1038/s41586-024-07441-w","kind":"article","title":"A whole-slide foundation model for digital pathology from real-world data","authors":["Xu H","Usuyama N","Bagga J","Zhang S","Rao R","Naumann T","Wong C","Gero Z","González J","Gu Y","Xu Y","Wei M","Wang W","Ma S","Wei F","Yang J","Li C","Gao J","Rosemon J","Bower T","Lee S","Weerasinghe R","Wright BJ","Robicsek A","Piening B"],"author_count":28,"year":2024,"published":"2024-05-22","venue":"Nature","cited_by":578,"doi":"10.1038/s41586-024-07441-w","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-024-07441-w","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-024-07441-w","updated":null,"pmid":"38778098","pmcid":"PMC11153137","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC11153137/pdf","abstract":"Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles 1-3 . Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important slide-level context 4 . Here we present Prov-GigaPath, a whole-slide pathology foundation model pretrained on 1.3 billion 256 × 256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres. The slides originated from more than 30,000 patients covering 31 major tissue types. To pretrain Prov-GigaPath, we propose GigaPath, a novel vision transformer architecture for pretraining gigapixel pathology slides. To scale GigaPath for slide-level learning with tens of thousands of image tiles, GigaPath adapts the newly developed LongNet 5 method to digital pathology. To evaluate Prov-GigaPath, we construct a digital pathology benchmark comprising 9 cancer subtyping tasks and 17 pathomics tasks, using both Providence and TCGA data 6 . With large-scale pretraining and ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance on 25 out of 26 tasks, with significant improvement over the second-best method on 18 tasks. We further demonstrate the potential of Prov-GigaPath on vision-language pretraining for pathology 7,8 by incorporating the pathology reports. In sum, Prov-GigaPath is an open-weight foundation model that achieves state-of-the-art performance on various digital pathology tasks, demonstrating the importance of real-world data and whole-slide modelling.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"A whole-slide foundation model for digital pathology from real-world data\" by Xu H, Usuyama N, Bagga J et al., https://doi.org/10.1038/s41586-024-07441-w, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"arxiv:1803.01229","kind":"article","title":"GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification","authors":["Maayan Frid-Adar","Idit Diamant","Eyal Klang","Michal Marianne Amitai","Jacob Goldberger","Hayit K. Greenspan"],"author_count":6,"year":2018,"published":"2018-09-21","venue":"Neurocomputing","cited_by":1950,"doi":"10.1016/j.neucom.2018.09.013","arxiv_id":"1803.01229","nct_id":null,"source_url":"https://doi.org/10.1016/j.neucom.2018.09.013","archive_url":"https://space-kitty.com/papers/arxiv:1803.01229","updated":"2018-03-03","pmid":null,"pmcid":null,"openalex_id":"W2794022343","pdf_url":"https://arxiv.org/pdf/1803.01229","abstract":"Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using large-scale annotated datasets. However, obtaining such datasets in the medical domain remains a challenge. In this paper, we present methods for generating synthetic medical images using recently presented deep learning Generative Adversarial Networks (GANs). Furthermore, we show that generated medical images can be used for synthetic data augmentation, and improve the performance of CNN for medical image classification. Our novel method is demonstrated on a limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). We first exploit GAN architectures for synthesizing high quality liver lesion ROIs. Then we present a novel scheme for liver lesion classification using CNN. Finally, we train the CNN using classic data augmentation and our synthetic data augmentation and compare performance. In addition, we explore the quality of our synthesized examples using visualization and expert assessment. The classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation the results increased to 85.7% sensitivity and 92.4% specificity. We believe that this approach to synthetic data augmentation can generalize to other medical classification applications and thus support radiologists' efforts to improve diagnosis.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Artificial Intelligence in Healthcare and Education"],"details":null,"provenance":"openalex+arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1007/s10278-019-00227-x","kind":"review","title":"Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges","authors":["Mohammad Hesam Hesamian","Wenjing Jia","Xiangjian He","Paul J Kennedy"],"author_count":4,"year":2019,"published":"2019-05-29","venue":"Journal of Imaging Informatics in Medicine","cited_by":1638,"doi":"10.1007/s10278-019-00227-x","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1007/s10278-019-00227-x","archive_url":"https://space-kitty.com/papers/doi:10.1007/s10278-019-00227-x","updated":null,"pmid":"31144149","pmcid":null,"openalex_id":"W2947263797","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10278-019-00227-x.pdf","abstract":"Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Medical Image Segmentation Techniques"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges\" by Mohammad Hesam Hesamian, Wenjing Jia, Xiangjian He et al., https://doi.org/10.1007/s10278-019-00227-x, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1148/rg.2017160130","kind":"review","title":"Machine Learning for Medical Imaging","authors":["Bradley James Erickson","Panagiotis D. Korfiatis","Zeynettin Akkus","Timothy Lee Kline"],"author_count":4,"year":2017,"published":"2017-02-17","venue":"Radiographics","cited_by":1672,"doi":"10.1148/rg.2017160130","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1148/rg.2017160130","archive_url":"https://space-kitty.com/papers/doi:10.1148/rg.2017160130","updated":null,"pmid":"28212054","pmcid":null,"openalex_id":"W2588978745","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Radiomics and Machine Learning in Medical Imaging"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1371/journal.pmed.1002686","kind":"article","title":"Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists","authors":["Pranav Rajpurkar","Jeremy Irvin","Robyn L. Ball","Kaylie Zhu","Brandon Yang","Hershel Mehta","Tony Duan","Daisy Yi Ding","Aarti Bagul","Curtis P. Langlotz","Bhavik Natvar Patel","Kristen W. Yeom","Katie S. Shpanskaya","Francis G. Blankenberg","Jayne M. Seekins","Timothy J. Amrhein","David Andrew Mong","Safwan S. Halabi","Evan James Zucker","Andrew Y. Ng","Matthew P. Lungren"],"author_count":21,"year":2018,"published":"2018-11-20","venue":"PLoS Medicine","cited_by":1449,"doi":"10.1371/journal.pmed.1002686","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1371/journal.pmed.1002686","archive_url":"https://space-kitty.com/papers/doi:10.1371/journal.pmed.1002686","updated":null,"pmid":"30457988","pmcid":null,"openalex_id":"W2901954625","pdf_url":"https://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.1002686&type=printable","abstract":"BACKGROUND: Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets. The purpose of this study is to investigate the performance of a deep learning algorithm on the detection of pathologies in chest radiographs compared with practicing radiologists. METHODS AND FINDINGS: We developed CheXNeXt, a convolutional neural network to concurrently detect the presence of 14 different pathologies, including pneumonia, pleural effusion, pulmonary masses, and nodules in frontal-view chest radiographs. CheXNeXt was trained and internally validated on the ChestX-ray8 dataset, with a held-out validation set consisting of 420 images, sampled to contain at least 50 cases of each of the original pathology labels. On this validation set, the majority vote of a panel of 3 board-certified cardiothoracic specialist radiologists served as reference standard. We compared CheXNeXt's discriminative performance on the validation set to the performance of 9 radiologists using the area under the receiver operating characteristic curve (AUC). The radiologists included 6 board-certified radiologists (average experience 12 years, range 4-28 years) and 3 senior radiology residents, from 3 academic institutions. We found that CheXNeXt achieved radiologist-level performance on 11 pathologies and did not achieve radiologist-level performance on 3 pathologies. The radiologists achieved statistically significantly higher AUC performance on cardiomegaly, emphysema, and hiatal hernia, with AUCs of 0.888 (95% confidence interval [CI] 0.863-0.910), 0.911 (95% CI 0.866-0.947), and 0.985 (95% CI 0.974-0.991), respectively, whereas CheXNeXt's AUCs were 0.831 (95% CI 0.790-0.870), 0.704 (95% CI 0.567-0.833), and 0.851 (95% CI 0.785-0.909), respectively. CheXNeXt performed better than radiologists in detecting atelectasis, with an AUC of 0.862 (95% CI 0.825-0.895), statistically significantly higher than radiologists' AUC of 0.808 (95% CI 0.777-0.838); there were no statistically significant differences in AUCs for the other 10 pathologies. The average time to interpret the 420 images in the validation set was substantially longer for the radiologists (240 minutes) than for CheXNeXt (1.5 minutes). The main limitations of our study are that neither CheXNeXt nor the radiologists were permitted to use patient history or review prior examinations and that evaluation was limited to a dataset from a single institution. CONCLUSIONS: In this study, we developed and validated a deep learning algorithm that classified clinically important abnormalities in chest radiographs at a performance level comparable to practicing radiologists. Once tested prospectively in clinical settings, the algorithm could have the potential to expand patient access to chest radiograph diagnostics.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["COVID-19 diagnosis using AI"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists\" by Pranav Rajpurkar, Jeremy Irvin, Robyn L. Ball et al., https://doi.org/10.1371/journal.pmed.1002686, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"arxiv:2105.13381","kind":"review","title":"Recent advances and clinical applications of deep learning in medical image analysis","authors":["Xuxin Chen","Ximin Wang","Ke Zhang","Kar-Ming A. Fung","Theresa Thai","Kathleen N. Moore","Robert S. Mannel","Hong Liu","Bin Zheng","Yuchen Qiu"],"author_count":10,"year":2022,"published":"2022-04-04","venue":"Medical Image Analysis","cited_by":1093,"doi":"10.1016/j.media.2022.102444","arxiv_id":"2105.13381","nct_id":null,"source_url":"https://doi.org/10.1016/j.media.2022.102444","archive_url":"https://space-kitty.com/papers/arxiv:2105.13381","updated":"2022-04-08","pmid":"35472844","pmcid":null,"openalex_id":"W3164581645","pdf_url":"https://arxiv.org/pdf/2105.13381","abstract":"Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis. Despite the success, the further improvement of deep learning models in medical image analysis is majorly bottlenecked by the lack of large-sized and well-annotated datasets. In the past five years, many studies have focused on addressing this challenge. In this paper, we reviewed and summarized these recent studies to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks. Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical image analysis, which are summarized based on different application scenarios, including classification, segmentation, detection, and image registration. We also discuss the major technical challenges and suggest the possible solutions in future research efforts.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Medical Image Segmentation Techniques"],"details":null,"provenance":"openalex+arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"arxiv:1709.02250","kind":"review","title":"Medical Image Analysis using Convolutional Neural Networks: A Review","authors":["Syed Muhammad Anwar","Muhammad Majid","Adnan Qayyum","Muhammad Awais","Majdi R. Alnowami","Muhammad Khurram Khan"],"author_count":6,"year":2018,"published":"2018-10-08","venue":"Journal of Medical Systems","cited_by":1431,"doi":"10.1007/s10916-018-1088-1","arxiv_id":"1709.02250","nct_id":null,"source_url":"https://doi.org/10.1007/s10916-018-1088-1","archive_url":"https://space-kitty.com/papers/arxiv:1709.02250","updated":"2019-05-21","pmid":"30298337","pmcid":null,"openalex_id":"W2752517284","pdf_url":"https://arxiv.org/pdf/1709.02250","abstract":"The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an effective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering has made medical image analysis one of the top research and development area. One of the reason for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This include application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Medical Image Segmentation Techniques"],"details":null,"provenance":"openalex+arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.3390/diagnostics13172760","kind":"review","title":"Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging","authors":["Reabal Najjar"],"author_count":1,"year":2023,"published":"2023-08-25","venue":"Diagnostics","cited_by":898,"doi":"10.3390/diagnostics13172760","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3390/diagnostics13172760","archive_url":"https://space-kitty.com/papers/doi:10.3390/diagnostics13172760","updated":null,"pmid":"37685300","pmcid":null,"openalex_id":"W4386222752","pdf_url":"https://www.mdpi.com/2075-4418/13/17/2760/pdf?version=1692969539","abstract":"This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It traces the evolution of radiology, from the initial discovery of X-rays to the application of machine learning and deep learning in modern medical image analysis. The primary focus of this review is to shed light on AI applications in radiology, elucidating their seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, with empirical evidence derived from a series of case studies across multiple medical disciplines. However, the integration of AI in radiology is not devoid of challenges. The review ventures into the labyrinth of obstacles that are inherent to AI-driven radiology-data quality, the 'black box' enigma, infrastructural and technical complexities, as well as ethical implications. Peering into the future, the review contends that the road ahead for AI in radiology is paved with promising opportunities. It advocates for continuous research, embracing avant-garde imaging technologies, and fostering robust collaborations between radiologists and AI developers. The conclusion underlines the role of AI as a catalyst for change in radiology, a stance that is firmly rooted in sustained innovation, dynamic partnerships, and a steadfast commitment to ethical responsibility.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Artificial Intelligence in Healthcare and Education"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging\" by Reabal Najjar, https://doi.org/10.3390/diagnostics13172760, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1007/s12194-017-0406-5","kind":"review","title":"Overview of deep learning in medical imaging","authors":["Kenji Suzuki"],"author_count":1,"year":2017,"published":"2017-07-08","venue":"Radiological Physics and Technology","cited_by":1095,"doi":"10.1007/s12194-017-0406-5","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1007/s12194-017-0406-5","archive_url":"https://space-kitty.com/papers/doi:10.1007/s12194-017-0406-5","updated":null,"pmid":"28689314","pmcid":null,"openalex_id":"W2731899572","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Artificial Intelligence in Healthcare and Education"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1186/s40537-019-0276-2","kind":"article","title":"Deep convolutional neural network based medical image classification for disease diagnosis","authors":["Samir S. Yadav","Shivajirao Manikrao Jadhav"],"author_count":2,"year":2019,"published":"2019-12-01","venue":"Journal Of Big Data","cited_by":1069,"doi":"10.1186/s40537-019-0276-2","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1186/s40537-019-0276-2","archive_url":"https://space-kitty.com/papers/doi:10.1186/s40537-019-0276-2","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2995942064","pdf_url":"https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-019-0276-2","abstract":"Abstract Medical image classification plays an essential role in clinical treatment and teaching tasks. However, the traditional method has reached its ceiling on performance. Moreover, by using them, much time and effort need to be spent on extracting and selecting classification features. The deep neural network is an emerging machine learning method that has proven its potential for different classification tasks. Notably, the convolutional neural network dominates with the best results on varying image classification tasks. However, medical image datasets are hard to collect because it needs a lot of professional expertise to label them. Therefore, this paper researches how to apply the convolutional neural network (CNN) based algorithm on a chest X-ray dataset to classify pneumonia. Three techniques are evaluated through experiments. These are linear support vector machine classifier with local rotation and orientation free features, transfer learning on two convolutional neural network models: Visual Geometry Group i.e., VGG16 and InceptionV3, and a capsule network training from scratch. Data augmentation is a data preprocessing method applied to all three methods. The results of the experiments show that data augmentation generally is an effective way for all three algorithms to improve performance. Also, Transfer learning is a more useful classification method on a small dataset compared to a support vector machine with oriented fast and rotated binary (ORB) robust independent elementary features and capsule network. In transfer learning, retraining specific features on a new target dataset is essential to improve performance. And, the second important factor is a proper network complexity that matches the scale of the dataset.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["COVID-19 diagnosis using AI"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Deep convolutional neural network based medical image classification for disease diagnosis\" by Samir S. Yadav, Shivajirao Manikrao Jadhav, https://doi.org/10.1186/s40537-019-0276-2, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"arxiv:2010.16056","kind":"article","title":"Generating Radiology Reports via Memory-driven Transformer","authors":["Zhihong Chen","Yan Song","Tsung-Hui Chang","Xiang Wan"],"author_count":4,"year":2020,"published":"2020-10-30","venue":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","cited_by":693,"doi":"10.18653/v1/2020.emnlp-main.112","arxiv_id":"2010.16056","nct_id":null,"source_url":"https://arxiv.org/abs/2010.16056","archive_url":"https://space-kitty.com/papers/arxiv:2010.16056","updated":"2022-04-28","pmid":null,"pmcid":null,"openalex_id":"W3104609094","pdf_url":"https://arxiv.org/pdf/2010.16056","abstract":"Medical imaging is frequently used in clinical practice and trials for diagnosis and treatment. Writing imaging reports is time-consuming and can be error-prone for inexperienced radiologists. Therefore, automatically generating radiology reports is highly desired to lighten the workload of radiologists and accordingly promote clinical automation, which is an essential task to apply artificial intelligence to the medical domain. In this paper, we propose to generate radiology reports with memory-driven Transformer, where a relational memory is designed to record key information of the generation process and a memory-driven conditional layer normalization is applied to incorporating the memory into the decoder of Transformer. Experimental results on two prevailing radiology report datasets, IU X-Ray and MIMIC-CXR, show that our proposed approach outperforms previous models with respect to both language generation metrics and clinical evaluations. Particularly, this is the first work reporting the generation results on MIMIC-CXR to the best of our knowledge. Further analyses also demonstrate that our approach is able to generate long reports with necessary medical terms as well as meaningful image-text attention mappings.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["cs.CL"],"details":null,"provenance":"arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"arxiv:2407.06508","kind":"article","title":"A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models","authors":["Gabriele Campanella","Shengjia Chen","Ruchika Verma","Jennifer Zeng","Aryeh Stock","Matt Croken","Brandon Veremis","Abdulkadir Elmas","Kuan-lin Huang","Ricky Kwan","Jane Houldsworth","Adam J. Schoenfeld","Chad Vanderbilt"],"author_count":13,"year":2024,"published":"2024-07-09","venue":"Nature Communications","cited_by":126,"doi":"10.1038/s41467-025-58796-1","arxiv_id":"2407.06508","nct_id":null,"source_url":"https://arxiv.org/abs/2407.06508","archive_url":"https://space-kitty.com/papers/arxiv:2407.06508","updated":"2024-07-11","pmid":null,"pmcid":null,"openalex_id":"W4409488923","pdf_url":"https://arxiv.org/pdf/2407.06508","abstract":"The use of self-supervised learning (SSL) to train pathology foundation models has increased substantially in the past few years. Notably, several models trained on large quantities of clinical data have been made publicly available in recent months. This will significantly enhance scientific research in computational pathology and help bridge the gap between research and clinical deployment. With the increase in availability of public foundation models of different sizes, trained using different algorithms on different datasets, it becomes important to establish a benchmark to compare the performance of such models on a variety of clinically relevant tasks spanning multiple organs and diseases. In this work, we present a collection of pathology datasets comprising clinical slides associated with clinically relevant endpoints including cancer diagnoses and a variety of biomarkers generated during standard hospital operation from two medical centers. We leverage these datasets to systematically assess the performance of public pathology foundation models and provide insights into best practices for training new foundation models and selecting appropriate pretrained models.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["eess.IV","cs.CV"],"details":null,"provenance":"arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1016/j.jdent.2018.07.015","kind":"article","title":"Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm","authors":["Jae‐Hong Lee","Do‐Hyung Kim","Seong‐Nyum Jeong","Seong‐Ho Choi"],"author_count":4,"year":2018,"published":"2018-07-26","venue":"Journal of Dentistry","cited_by":1004,"doi":"10.1016/j.jdent.2018.07.015","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.jdent.2018.07.015","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.jdent.2018.07.015","updated":null,"pmid":"30056118","pmcid":null,"openalex_id":"W2883741661","pdf_url":null,"abstract":null,"abstract_license":null,"license":"cc-by-nc-nd","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Dental Radiography and Imaging"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.7150/thno.30309","kind":"review","title":"The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges","authors":["Zhenyu Liu","Shuo Wang","Di Dong","Jingwei Wei","Cheng Fang","Xuezhi Zhou","Kai Sun","Longfei Li","Bo Li","Meiyun Wang","Jie Tian"],"author_count":11,"year":2019,"published":"2019-01-01","venue":"Theranostics","cited_by":971,"doi":"10.7150/thno.30309","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.7150/thno.30309","archive_url":"https://space-kitty.com/papers/doi:10.7150/thno.30309","updated":null,"pmid":"30867832","pmcid":null,"openalex_id":"W2917364154","pdf_url":"https://www.thno.org/v09p1303.pdf","abstract":null,"abstract_license":null,"license":"cc-by-nc","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Radiomics and Machine Learning in Medical Imaging"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1038/s41746-020-00341-z","kind":"review","title":"Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines","authors":["Shih-Cheng Huang","Anuj Pareek","Saeed Seyyedi","Imon Banerjee","Matthew P. Lungren"],"author_count":5,"year":2020,"published":"2020-10-16","venue":"npj Digital Medicine","cited_by":930,"doi":"10.1038/s41746-020-00341-z","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41746-020-00341-z","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41746-020-00341-z","updated":null,"pmid":"33083571","pmcid":null,"openalex_id":"W3094595351","pdf_url":"https://www.nature.com/articles/s41746-020-00341-z.pdf","abstract":"Advancements in deep learning techniques carry the potential to make significant contributions to healthcare, particularly in fields that utilize medical imaging for diagnosis, prognosis, and treatment decisions. The current state-of-the-art deep learning models for radiology applications consider only pixel-value information without data informing clinical context. Yet in practice, pertinent and accurate non-imaging data based on the clinical history and laboratory data enable physicians to interpret imaging findings in the appropriate clinical context, leading to a higher diagnostic accuracy, informative clinical decision making, and improved patient outcomes. To achieve a similar goal using deep learning, medical imaging pixel-based models must also achieve the capability to process contextual data from electronic health records (EHR) in addition to pixel data. In this paper, we describe different data fusion techniques that can be applied to combine medical imaging with EHR, and systematically review medical data fusion literature published between 2012 and 2020. We conducted a systematic search on PubMed and Scopus for original research articles leveraging deep learning for fusion of multimodality data. In total, we screened 985 studies and extracted data from 17 papers. By means of this systematic review, we present current knowledge, summarize important results and provide implementation guidelines to serve as a reference for researchers interested in the application of multimodal fusion in medical imaging.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Healthcare"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines\" by Shih-Cheng Huang, Anuj Pareek, Saeed Seyyedi et al., https://doi.org/10.1038/s41746-020-00341-z, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41591-025-03780-x","kind":"article","title":"Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection","authors":["Campanella G","Kumar N","Nanda S","Singi S","Fluder E","Kwan R","Muehlstedt S","Pfarr N","Schüffler PJ","Häggström I","Neittaanmäki N","Akyürek LM","Basnet A","Jamaspishvili T","Nasr MR","Croken MM","Hirsch FR","Elkrief A","Yu H","Ardon O","Goldgof GM","Hameed M","Houldsworth J","Arcila M","Fuchs TJ"],"author_count":26,"year":2025,"published":"2025-07-09","venue":"Nature medicine","cited_by":66,"doi":"10.1038/s41591-025-03780-x","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41591-025-03780-x","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41591-025-03780-x","updated":null,"pmid":"40634781","pmcid":"PMC12443599","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC12443599/pdf","abstract":"Artificial intelligence models using digital histopathology slides stained with hematoxylin and eosin offer promising, tissue-preserving diagnostic tools for patients with cancer. Despite their advantages, their clinical utility in real-world settings remains unproven. Assessing EGFR mutations in lung adenocarcinoma demands rapid, accurate and cost-effective tests that preserve tissue for genomic sequencing. PCR-based assays provide rapid results but with reduced accuracy compared with next-generation sequencing and require additional tissue. Computational biomarkers leveraging modern foundation models can address these limitations. Here we assembled a large international clinical dataset of digital lung adenocarcinoma slides (N = 8,461) to develop a computational EGFR biomarker. Our model fine-tunes an open-source foundation model, improving task-specific performance with out-of-center generalization and clinical-grade accuracy on primary and metastatic specimens (mean area under the curve: internal 0.847, external 0.870). To evaluate real-world clinical translation, we conducted a prospective silent trial of the biomarker on primary samples, achieving an area under the curve of 0.890. The artificial-intelligence-assisted workflow reduced the number of rapid molecular tests needed by up to 43% while maintaining the current clinical standard performance. Our retrospective and prospective analyses demonstrate the real-world clinical utility of a computational pathology biomarker.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection\" by Campanella G, Kumar N, Nanda S et al., https://doi.org/10.1038/s41591-025-03780-x, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1186/s41747-018-0061-6","kind":"review","title":"Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine","authors":["Filippo Pesapane","Marina Codari","Francesco Sardanelli"],"author_count":3,"year":2018,"published":"2018-09-29","venue":"European Radiology Experimental","cited_by":839,"doi":"10.1186/s41747-018-0061-6","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1186/s41747-018-0061-6","archive_url":"https://space-kitty.com/papers/doi:10.1186/s41747-018-0061-6","updated":null,"pmid":"30353365","pmcid":null,"openalex_id":"W2893693469","pdf_url":"https://eurradiolexp.springeropen.com/track/pdf/10.1186/s41747-018-0061-6","abstract":"One of the most promising areas of health innovation is the application of artificial intelligence (AI), primarily in medical imaging. This article provides basic definitions of terms such as \"machine/deep learning\" and analyses the integration of AI into radiology. Publications on AI have drastically increased from about 100-150 per year in 2007-2008 to 700-800 per year in 2016-2017. Magnetic resonance imaging and computed tomography collectively account for more than 50% of current articles. Neuroradiology appears in about one-third of the papers, followed by musculoskeletal, cardiovascular, breast, urogenital, lung/thorax, and abdomen, each representing 6-9% of articles. With an irreversible increase in the amount of data and the possibility to use AI to identify findings either detectable or not by the human eye, radiology is now moving from a subjective perceptual skill to a more objective science. Radiologists, who were on the forefront of the digital era in medicine, can guide the introduction of AI into healthcare. Yet, they will not be replaced because radiology includes communication of diagnosis, consideration of patient's values and preferences, medical judgment, quality assurance, education, policy-making, and interventional procedures. The higher efficiency provided by AI will allow radiologists to perform more value-added tasks, becoming more visible to patients and playing a vital role in multidisciplinary clinical teams.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Artificial Intelligence in Healthcare and Education"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine\" by Filippo Pesapane, Marina Codari, Francesco Sardanelli, https://doi.org/10.1186/s41747-018-0061-6, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.3390/bioengineering10121435","kind":"review","title":"How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications","authors":["Luis Pinto Coelho"],"author_count":1,"year":2023,"published":"2023-12-18","venue":"Bioengineering","cited_by":688,"doi":"10.3390/bioengineering10121435","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3390/bioengineering10121435","archive_url":"https://space-kitty.com/papers/doi:10.3390/bioengineering10121435","updated":null,"pmid":"38136026","pmcid":null,"openalex_id":"W4389885061","pdf_url":"https://www.mdpi.com/2306-5354/10/12/1435/pdf?version=1702863805","abstract":"The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Artificial Intelligence in Healthcare and Education"],"details":null,"provenance":"openalex","attribution":"Abstract of \"How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications\" by Luis Pinto Coelho, https://doi.org/10.3390/bioengineering10121435, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1016/j.eng.2018.11.020","kind":"article","title":"Deep Learning in Medical Ultrasound Analysis: A Review","authors":["Shengfeng Liu","Yi Wang","Xin Yang","Baiying Lei","Li Liu","Shawn Xiang Li","Dong Ni","Tianfu Wang"],"author_count":8,"year":2019,"published":"2019-01-29","venue":"Engineering","cited_by":796,"doi":"10.1016/j.eng.2018.11.020","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.eng.2018.11.020","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.eng.2018.11.020","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2913559493","pdf_url":"https://www.sciencedirect.com/science/article/pii/S2095809918301887/pdf","abstract":null,"abstract_license":null,"license":"cc-by-nc-nd","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Medical Image Segmentation Techniques"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.3389/fmed.2020.00027","kind":"article","title":"Artificial Intelligence in Medicine: Today and Tomorrow","authors":["Giovanni Briganti","Olivier Le Moine"],"author_count":2,"year":2020,"published":"2020-02-04","venue":"Frontiers in Medicine","cited_by":741,"doi":"10.3389/fmed.2020.00027","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3389/fmed.2020.00027","archive_url":"https://space-kitty.com/papers/doi:10.3389/fmed.2020.00027","updated":null,"pmid":"32118012","pmcid":null,"openalex_id":"W3005335661","pdf_url":"https://fjfsdata01prod.blob.core.windows.net/articles/files/509744/pubmed-zip/.versions/1/.package-entries/fmed-07-00027/fmed-07-00027.pdf?sv=2018-03-28&sr=b&sig=zXufvwUpJlfZXE%2F4dbleEHEnGFyGGLQglOLwue7CRU0%3D&se=2021-02-18T19%3A03%3A16Z&sp=r&rscd=attachment%3B%20filename%2A%3DUTF-8%27%27fmed-07-00027.pdf","abstract":"Artificial intelligence-powered medical technologies are rapidly evolving into applicable solutions for clinical practice. Deep learning algorithms can deal with increasing amounts of data provided by wearables, smartphones, and other mobile monitoring sensors in different areas of medicine. Currently, only very specific settings in clinical practice benefit from the application of artificial intelligence, such as the detection of atrial fibrillation, epilepsy seizures, and hypoglycemia, or the diagnosis of disease based on histopathological examination or medical imaging. The implementation of augmented medicine is long-awaited by patients because it allows for a greater autonomy and a more personalized treatment, however, it is met with resistance from physicians which were not prepared for such an evolution of clinical practice. This phenomenon also creates the need to validate these modern tools with traditional clinical trials, debate the educational upgrade of the medical curriculum in light of digital medicine as well as ethical consideration of the ongoing connected monitoring. The aim of this paper is to discuss recent scientific literature and provide a perspective on the benefits, future opportunities and risks of established artificial intelligence applications in clinical practice on physicians, healthcare institutions, medical education, and bioethics.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Artificial Intelligence in Healthcare and Education"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Artificial Intelligence in Medicine: Today and Tomorrow\" by Giovanni Briganti, Olivier Le Moine, https://doi.org/10.3389/fmed.2020.00027, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/srep24454","kind":"article","title":"Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans","authors":["Jie-Zhi Cheng","Dong Ni","Yi‐Hong Chou","Jing Qin","Chui‐Mei Tiu","Yeun‐Chung Chang","Chiun‐Sheng Huang","Dinggang G. Shen","Chung‐Ming Chen"],"author_count":9,"year":2016,"published":"2016-04-15","venue":"Scientific Reports","cited_by":747,"doi":"10.1038/srep24454","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/srep24454","archive_url":"https://space-kitty.com/papers/doi:10.1038/srep24454","updated":null,"pmid":"27079888","pmcid":null,"openalex_id":"W2341106171","pdf_url":"https://www.nature.com/articles/srep24454.pdf","abstract":"This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["AI in cancer detection"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans\" by Jie-Zhi Cheng, Dong Ni, Yi‐Hong Chou et al., https://doi.org/10.1038/srep24454, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.3390/bios12080562","kind":"review","title":"Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare","authors":["Pandiaraj Manickam","Siva Ananth Mariappan","Sindhu Monica Murugesan","Shekhar Hansda","Ajeet Kumar Kaushik","Ravikumar Bapurao Shinde","S. P. Thipperudraswamy"],"author_count":7,"year":2022,"published":"2022-07-25","venue":"Biosensors","cited_by":641,"doi":"10.3390/bios12080562","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3390/bios12080562","archive_url":"https://space-kitty.com/papers/doi:10.3390/bios12080562","updated":null,"pmid":"35892459","pmcid":null,"openalex_id":"W4287834747","pdf_url":"https://www.mdpi.com/2079-6374/12/8/562/pdf?version=1658743063","abstract":"Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep learning (DL), conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences. Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things (IoMT) emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care (POC) devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management. The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article. The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review. This review also encompasses the technological and engineering challenges and prospects for AI-based cloud-integrated personalized IoMT devices for designing efficient POC biomedical systems suitable for next-generation intelligent healthcare.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Internet of Things and AI"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare\" by Pandiaraj Manickam, Siva Ananth Mariappan, Sindhu Monica Murugesan et al., https://doi.org/10.3390/bios12080562, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."}],"next":"https://space-kitty.com/api/v1/topics/ai-medicine/papers?sort=top&limit=25&offset=25"}