{"topic":"neuroai","sort":"top","limit":25,"offset":0,"count":25,"papers":[{"id":"arxiv:1505.03540","kind":"article","title":"Brain tumor segmentation with Deep Neural Networks","authors":["Mohammad Havaei","Axel Davy","David Warde-Farley","Antoine Biard","Aaron C. Courville","Yoshua Bengio","Christopher Pal","Pierre‐Marc Jodoin","Hugo Larochelle"],"author_count":9,"year":2016,"published":"2016-05-21","venue":"Medical Image Analysis","cited_by":3277,"doi":"10.1016/j.media.2016.05.004","arxiv_id":"1505.03540","nct_id":null,"source_url":"https://doi.org/10.1016/j.media.2016.05.004","archive_url":"https://space-kitty.com/papers/arxiv:1505.03540","updated":"2016-05-20","pmid":"27310171","pmcid":null,"openalex_id":"W1884191083","pdf_url":"https://arxiv.org/pdf/1505.03540","abstract":"In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test dataset reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Brain Tumor Detection and Classification"],"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:10.1109/tmi.2016.2538465","kind":"article","title":"Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images","authors":["Sérgio Pereira","Adriano B. Pinto","Victor Alves","Carlos A. Silva"],"author_count":4,"year":2016,"published":"2016-03-04","venue":"IEEE Transactions on Medical Imaging","cited_by":2735,"doi":"10.1109/tmi.2016.2538465","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1109/tmi.2016.2538465","archive_url":"https://space-kitty.com/papers/doi:10.1109/tmi.2016.2538465","updated":null,"pmid":"26960222","pmcid":null,"openalex_id":"W2310992461","pdf_url":null,"abstract":null,"abstract_license":null,"license":"cc-by-nc-nd","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Brain Tumor Detection and Classification"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1146/annurev-vision-082114-035447","kind":"review","title":"Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing","authors":["Nikolaus Kriegeskorte"],"author_count":1,"year":2015,"published":"2015-11-18","venue":"Annual Review of Vision Science","cited_by":1083,"doi":"10.1146/annurev-vision-082114-035447","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1146/annurev-vision-082114-035447","archive_url":"https://space-kitty.com/papers/doi:10.1146/annurev-vision-082114-035447","updated":null,"pmid":"28532370","pmcid":null,"openalex_id":"W2176287621","pdf_url":"https://www.annualreviews.org/doi/pdf/10.1146/annurev-vision-082114-035447","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Advanced Neural Network Applications"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1016/j.jocs.2018.12.003","kind":"article","title":"Multi-grade brain tumor classification using deep CNN with extensive data augmentation","authors":["Muhammad Sajjad","Salman Khan","Khan Muhammad","Wanqing Wu","Amin Ullah","Sung Wook Baik"],"author_count":6,"year":2018,"published":"2018-12-12","venue":"Journal of Computational Science","cited_by":895,"doi":"10.1016/j.jocs.2018.12.003","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.jocs.2018.12.003","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.jocs.2018.12.003","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2905017682","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Brain Tumor Detection and Classification"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"arxiv:1612.02572","kind":"article","title":"Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker","authors":["James Howard Cole","Rudra P. K. Poudel","Dimosthenis Tsagkrasoulis","Matthan W.A. Caan","Claire Joanne Steves","Tim D. Spector","Giovanni Montana"],"author_count":7,"year":2017,"published":"2017-07-29","venue":"NeuroImage","cited_by":870,"doi":"10.1016/j.neuroimage.2017.07.059","arxiv_id":"1612.02572","nct_id":null,"source_url":"https://doi.org/10.1016/j.neuroimage.2017.07.059","archive_url":"https://space-kitty.com/papers/arxiv:1612.02572","updated":"2016-12-08","pmid":"28765056","pmcid":null,"openalex_id":"W2579617530","pdf_url":"https://arxiv.org/pdf/1612.02572","abstract":"Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people and deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of \"brain-predicted age\" as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. Brain-predicted age represents an accurate, highly reliable and genetically-valid phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Dementia and Cognitive Impairment Research"],"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:10.1016/j.compmedimag.2019.05.001","kind":"article","title":"Brain tumor classification for MR images using transfer learning and fine-tuning","authors":["Zar Nawab Khan Swati","Qinghua Zhao","Muhammad Kabir","Farman Ali","Zakir Ali","Saeed Ahmad","Jianfeng Lu"],"author_count":7,"year":2019,"published":"2019-05-18","venue":"Computerized Medical Imaging and Graphics","cited_by":843,"doi":"10.1016/j.compmedimag.2019.05.001","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.compmedimag.2019.05.001","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.compmedimag.2019.05.001","updated":null,"pmid":"31150950","pmcid":null,"openalex_id":"W2945839551","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Brain Tumor Detection and Classification"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"arxiv:1704.07239","kind":"article","title":"MR-based synthetic CT generation using a deep convolutional neural network method","authors":["Xiao Han"],"author_count":1,"year":2017,"published":"2017-02-13","venue":"Medical Physics","cited_by":868,"doi":"10.1002/mp.12155","arxiv_id":"1704.07239","nct_id":null,"source_url":"https://doi.org/10.1002/mp.12155","archive_url":"https://space-kitty.com/papers/arxiv:1704.07239","updated":"2017-04-24","pmid":"28192624","pmcid":null,"openalex_id":"W2607711656","pdf_url":"https://arxiv.org/pdf/1704.07239","abstract":"Liver lesion segmentation is an important step for liver cancer diagnosis, treatment planning and treatment evaluation. LiTS (Liver Tumor Segmentation Challenge) provides a common testbed for comparing different automatic liver lesion segmentation methods. We participate in this challenge by developing a deep convolutional neural network (DCNN) method. The particular DCNN model works in 2.5D in that it takes a stack of adjacent slices as input and produces the segmentation map corresponding to the center slice. The model has 32 layers in total and makes use of both long range concatenation connections of U-Net [1] and short-range residual connections from ResNet [2]. The model was trained using the 130 LiTS training datasets and achieved an average Dice score of 0.67 when evaluated on the 70 test CT scans, which ranked first for the LiTS challenge at the time of the ISBI 2017 conference.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Advanced Radiotherapy 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:1904.07773","kind":"review","title":"Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation","authors":["Junhao Wen","Elina Thibeau–Sutre","Mauricio Díaz","Jorge Samper‐Gonzàlez","Alexandre Routier","Simona Bottani","Didier Dormont","Stanley Durrleman","Ninon Burgos","Olivier Colliot"],"author_count":10,"year":2020,"published":"2020-04-30","venue":"Medical Image Analysis","cited_by":801,"doi":"10.1016/j.media.2020.101694","arxiv_id":"1904.07773","nct_id":null,"source_url":"https://doi.org/10.1016/j.media.2020.101694","archive_url":"https://space-kitty.com/papers/arxiv:1904.07773","updated":"2020-06-01","pmid":"32417716","pmcid":null,"openalex_id":"W3022315685","pdf_url":"https://www.sciencedirect.com/science/article/am/pii/S1361841520300591?via%3Dihub","abstract":"Over 30 papers have proposed to use convolutional neural network (CNN) for AD classification from anatomical MRI. However, the classification performance is difficult to compare across studies due to variations in components such as participant selection, image preprocessing or validation procedure. Moreover, these studies are hardly reproducible because their frameworks are not publicly accessible and because implementation details are lacking. Lastly, some of these papers may report a biased performance due to inadequate or unclear validation or model selection procedures. In the present work, we aim to address these limitations through three main contributions. First, we performed a systematic literature review and found that more than half of the surveyed papers may have suffered from data leakage. Our second contribution is the extension of our open-source framework for classification of AD using CNN and T1-weighted MRI. Finally, we used this framework to rigorously compare different CNN architectures. The data was split into training/validation/test sets at the very beginning and only the training/validation sets were used for model selection. To avoid any overfitting, the test sets were left untouched until the end of the peer-review process. Overall, the different 3D approaches (3D-subject, 3D-ROI, 3D-patch) achieved similar performances while that of the 2D slice approach was lower. Of note, the different CNN approaches did not perform better than a SVM with voxel-based features. The different approaches generalized well to similar populations but not to datasets with different inclusion criteria or demographical characteristics.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Dementia and Cognitive Impairment Research"],"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:1406.3284","kind":"article","title":"Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition","authors":["Charles F. Cadieu","Ha Hong","Daniel L. K. Yamins","Nicolas Pinto","Diego Ardila","Ethan A. Solomon","Najib J. Majaj","James J. DiCarlo"],"author_count":8,"year":2014,"published":"2014-12-18","venue":"PLoS Computational Biology","cited_by":824,"doi":"10.1371/journal.pcbi.1003963","arxiv_id":"1406.3284","nct_id":null,"source_url":"https://doi.org/10.1371/journal.pcbi.1003963","archive_url":"https://space-kitty.com/papers/arxiv:1406.3284","updated":"2014-06-12","pmid":"25521294","pmcid":null,"openalex_id":"W2166206801","pdf_url":"https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003963&type=printable","abstract":"The primate visual system achieves remarkable visual object recognition performance even in brief presentations and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition). This remarkable performance is mediated by the representation formed in inferior temporal (IT) cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs). It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations such as the amount of noise, the number of neural recording sites, and the number trials, and computational limitations such as the complexity of the decoding classifier and the number of classifier training examples. In this work we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of \"kernel analysis\" that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Visual perception and processing mechanisms"],"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:10.1016/j.neuroimage.2014.12.061","kind":"article","title":"Deep convolutional neural networks for multi-modality isointense infant brain image segmentation","authors":["Wenlu Zhang","Rongjian Li","Houtao Deng","Li Wang","Weili Lin","Shuiwang Ji","Dinggang G. Shen"],"author_count":7,"year":2015,"published":"2015-01-03","venue":"NeuroImage","cited_by":817,"doi":"10.1016/j.neuroimage.2014.12.061","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.neuroimage.2014.12.061","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.neuroimage.2014.12.061","updated":null,"pmid":"25562829","pmcid":null,"openalex_id":"W2082526668","pdf_url":null,"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":"arxiv:1702.04528","kind":"article","title":"A deep learning model integrating FCNNs and CRFs for brain tumor segmentation","authors":["Xiaomei Zhao","Yihong Wu","Guidong Song","Zhenye Li","Yazhuo Zhang","Yong Fan A"],"author_count":6,"year":2017,"published":"2017-10-11","venue":"Medical Image Analysis","cited_by":813,"doi":"10.1016/j.media.2017.10.002","arxiv_id":"1702.04528","nct_id":null,"source_url":"https://doi.org/10.1016/j.media.2017.10.002","archive_url":"https://space-kitty.com/papers/arxiv:1702.04528","updated":"2017-11-10","pmid":"29040911","pmcid":null,"openalex_id":"W2587828787","pdf_url":"https://arxiv.org/pdf/1702.04528","abstract":"Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis, treatment planning, and treatment outcome evaluation. Build upon successful deep learning techniques, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNNs) and Conditional Random Fields (CRFs) in a unified framework to obtain segmentation results with appearance and spatial consistency. We train a deep learning based segmentation model using 2D image patches and image slices in following steps: 1) training FCNNs using image patches; 2) training CRFs as Recurrent Neural Networks (CRF-RNN) using image slices with parameters of FCNNs fixed; and 3) fine-tuning the FCNNs and the CRF-RNN using image slices. Particularly, we train 3 segmentation models using 2D image patches and slices obtained in axial, coronal and sagittal views respectively, and combine them to segment brain tumors using a voting based fusion strategy. Our method could segment brain images slice-by-slice, much faster than those based on image patches. We have evaluated our method based on imaging data provided by the Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2013, BRATS 2015 and BRATS 2016. The experimental results have demonstrated that our method could build a segmentation model with Flair, T1c, and T2 scans and achieve competitive performance as those built with Flair, T1, T1c, and T2 scans.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Brain Tumor Detection and Classification"],"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:10.1016/j.compmedimag.2021.102026","kind":"article","title":"Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation","authors":["Michael Yeung","Evis Sala","Carola‐Bibiane Schönlieb","Leonardo Rundo"],"author_count":4,"year":2021,"published":"2021-12-13","venue":"Computerized Medical Imaging and Graphics","cited_by":748,"doi":"10.1016/j.compmedimag.2021.102026","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.compmedimag.2021.102026","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.compmedimag.2021.102026","updated":null,"pmid":"34953431","pmcid":null,"openalex_id":"W3160412534","pdf_url":"https://www.sciencedirect.com/science/article/pii/S0895611121001750/pdf","abstract":"Automatic segmentation methods are an important advancement in medical image analysis. Machine learning techniques, and deep neural networks in particular, are the state-of-the-art for most medical image segmentation tasks. Issues with class imbalance pose a significant challenge in medical datasets, with lesions often occupying a considerably smaller volume relative to the background. Loss functions used in the training of deep learning algorithms differ in their robustness to class imbalance, with direct consequences for model convergence. The most commonly used loss functions for segmentation are based on either the cross entropy loss, Dice loss or a combination of the two. We propose the Unified Focal loss, a new hierarchical framework that generalises Dice and cross entropy-based losses for handling class imbalance. We evaluate our proposed loss function on five publicly available, class imbalanced medical imaging datasets: CVC-ClinicDB, Digital Retinal Images for Vessel Extraction (DRIVE), Breast Ultrasound 2017 (BUS2017), Brain Tumour Segmentation 2020 (BraTS20) and Kidney Tumour Segmentation 2019 (KiTS19). We compare our loss function performance against six Dice or cross entropy-based loss functions, across 2D binary, 3D binary and 3D multiclass segmentation tasks, demonstrating that our proposed loss function is robust to class imbalance and consistently outperforms the other loss functions. Source code is available at: https://github.com/mlyg/unified-focal-loss.","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 \"Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation\" by Michael Yeung, Evis Sala, Carola‐Bibiane Schönlieb et al., https://doi.org/10.1016/j.compmedimag.2021.102026, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1109/access.2019.2919122","kind":"article","title":"Multi-Classification of Brain Tumor Images Using Deep Neural Network","authors":["Hossam H. Sultan","Nancy M. Salem","Walid Al‐Atabany"],"author_count":3,"year":2019,"published":"2019-01-01","venue":"IEEE Access","cited_by":774,"doi":"10.1109/access.2019.2919122","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1109/access.2019.2919122","archive_url":"https://space-kitty.com/papers/doi:10.1109/access.2019.2919122","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2947735999","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08723045.pdf","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Brain Tumor Detection and Classification"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1038/s41598-020-74399-w","kind":"article","title":"Multimodal deep learning models for early detection of Alzheimer’s disease stage","authors":["Janani Venugopalan","Tong Li","Hamid Reza Hassanzadeh","May Dongmei Wang"],"author_count":4,"year":2021,"published":"2021-02-05","venue":"Scientific Reports","cited_by":732,"doi":"10.1038/s41598-020-74399-w","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41598-020-74399-w","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41598-020-74399-w","updated":null,"pmid":"33547343","pmcid":null,"openalex_id":"W3127597934","pdf_url":"https://www.nature.com/articles/s41598-020-74399-w.pdf","abstract":"Most current Alzheimer's disease (AD) and mild cognitive disorders (MCI) studies use single data modality to make predictions such as AD stages. The fusion of multiple data modalities can provide a holistic view of AD staging analysis. Thus, we use deep learning (DL) to integrally analyze imaging (magnetic resonance imaging (MRI)), genetic (single nucleotide polymorphisms (SNPs)), and clinical test data to classify patients into AD, MCI, and controls (CN). We use stacked denoising auto-encoders to extract features from clinical and genetic data, and use 3D-convolutional neural networks (CNNs) for imaging data. We also develop a novel data interpretation method to identify top-performing features learned by the deep-models with clustering and perturbation analysis. Using Alzheimer's disease neuroimaging initiative (ADNI) dataset, we demonstrate that deep models outperform shallow models, including support vector machines, decision trees, random forests, and k-nearest neighbors. In addition, we demonstrate that integrating multi-modality data outperforms single modality models in terms of accuracy, precision, recall, and meanF1 scores. Our models have identified hippocampus, amygdala brain areas, and the Rey Auditory Verbal Learning Test (RAVLT) as top distinguished features, which are consistent with the known AD literature.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Dementia and Cognitive Impairment Research"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Multimodal deep learning models for early detection of Alzheimer’s disease stage\" by Janani Venugopalan, Tong Li, Hamid Reza Hassanzadeh et al., https://doi.org/10.1038/s41598-020-74399-w, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1037/pspa0000098","kind":"article","title":"Deep neural networks are more accurate than humans at detecting sexual orientation from facial images.","authors":["Yilun Wang","Michał Kosiński"],"author_count":2,"year":2018,"published":"2018-02-01","venue":"Journal of Personality and Social Psychology","cited_by":766,"doi":"10.1037/pspa0000098","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1037/pspa0000098","archive_url":"https://space-kitty.com/papers/doi:10.1037/pspa0000098","updated":null,"pmid":"29389215","pmcid":null,"openalex_id":"W2781265533","pdf_url":null,"abstract":"We show that faces contain much more information about sexual orientation than can be perceived or interpreted by the human brain. We used deep neural networks to extract features from 35,326 facial images. These features were entered into a logistic regression aimed at classifying sexual orientation. Given a single facial image, a classifier could correctly distinguish between gay and heterosexual men in 81% of cases, and in 71% of cases for women. Human judges achieved much lower accuracy: 61% for men and 54% for women. The accuracy of the algorithm increased to 91% and 83%, respectively, given five facial images per person. Facial features employed by the classifier included both fixed (e.g., nose shape) and transient facial features (e.g., grooming style). Consistent with the prenatal hormone theory of sexual orientation, gay men and women tended to have gender-atypical facial morphology, expression, and grooming styles. Prediction models aimed at gender alone allowed for detecting gay males with 57% accuracy and gay females with 58% accuracy. Those findings advance our understanding of the origins of sexual orientation and the limits of human perception. Additionally, given that companies and governments are increasingly using computer vision algorithms to detect people's intimate traits, our findings expose a threat to the privacy and safety of gay men and women. (PsycINFO Database Record","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Face Recognition and Perception"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Deep neural networks are more accurate than humans at detecting sexual orientation from facial images.\" by Yilun Wang, Michał Kosiński, https://doi.org/10.1037/pspa0000098, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41598-021-90428-8","kind":"article","title":"Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images","authors":["Ramin Ranjbarzadeh","Abbas Bagherian Kasgari","Saeid Jafarzadeh Ghoushchi","Shokofeh Anari","Maryam Naseri","Malika Bendechache"],"author_count":6,"year":2021,"published":"2021-05-25","venue":"Scientific Reports","cited_by":643,"doi":"10.1038/s41598-021-90428-8","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41598-021-90428-8","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41598-021-90428-8","updated":null,"pmid":"34035406","pmcid":null,"openalex_id":"W3164956625","pdf_url":"https://www.nature.com/articles/s41598-021-90428-8.pdf","abstract":"Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each slice, a simple and efficient Cascade Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. This C-CNN model mines both local and global features in two different routes. Also, to improve the brain tumor segmentation accuracy compared with the state-of-the-art models, a novel Distance-Wise Attention (DWA) mechanism is introduced. The DWA mechanism considers the effect of the center location of the tumor and the brain inside the model. Comprehensive experiments are conducted on the BRATS 2018 dataset and show that the proposed model obtains competitive results: the proposed method achieves a mean whole tumor, enhancing tumor, and tumor core dice scores of 0.9203, 0.9113 and 0.8726 respectively. Other quantitative and qualitative assessments are presented and discussed.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Brain Tumor Detection and Classification"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images\" by Ramin Ranjbarzadeh, Abbas Bagherian Kasgari, Saeid Jafarzadeh Ghoushchi et al., https://doi.org/10.1038/s41598-021-90428-8, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/srep27755","kind":"article","title":"Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence","authors":["Radoslaw Martin Cichy","Aditya Khosla","Dimitrios Pantazis","Antonio B. Torralba","Aude Oliva"],"author_count":5,"year":2016,"published":"2016-06-10","venue":"Scientific Reports","cited_by":717,"doi":"10.1038/srep27755","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/srep27755","archive_url":"https://space-kitty.com/papers/doi:10.1038/srep27755","updated":null,"pmid":"27282108","pmcid":null,"openalex_id":"W2412480261","pdf_url":"https://www.nature.com/articles/srep27755.pdf","abstract":"The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Visual perception and processing mechanisms"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence\" by Radoslaw Martin Cichy, Aditya Khosla, Dimitrios Pantazis et al., https://doi.org/10.1038/srep27755, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"arxiv:1705.07664","kind":"article","title":"CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters","authors":["Ron Levie","Federico Monti","Xavier Bresson","Michael M. Bronstein"],"author_count":4,"year":2018,"published":"2018-11-05","venue":"IEEE Transactions on Signal Processing","cited_by":686,"doi":"10.1109/tsp.2018.2879624","arxiv_id":"1705.07664","nct_id":null,"source_url":"https://doi.org/10.1109/tsp.2018.2879624","archive_url":"https://space-kitty.com/papers/arxiv:1705.07664","updated":"2018-10-31","pmid":null,"pmcid":null,"openalex_id":"W2618170429","pdf_url":"https://arxiv.org/pdf/1705.07664","abstract":"The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains. In this paper, we introduce a new spectral domain convolutional architecture for deep learning on graphs. The core ingredient of our model is a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest. Our model generates rich spectral filters that are localized in space, scales linearly with the size of the input data for sparsely-connected graphs, and can handle different constructions of Laplacian operators. Extensive experimental results show the superior performance of our approach, in comparison to other spectral domain convolutional architectures, on spectral image classification, community detection, vertex classification and matrix completion tasks.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Advanced Graph Neural Networks"],"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:1807.07356","kind":"article","title":"Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks","authors":["Guotai Wang","Wenqi Li","Michaël Aertsen","Jan A. Deprest","Sebastien Ourselin A","Tom K. Vercauteren"],"author_count":6,"year":2019,"published":"2019-02-10","venue":"Neurocomputing","cited_by":657,"doi":"10.1016/j.neucom.2019.01.103","arxiv_id":"1807.07356","nct_id":null,"source_url":"https://doi.org/10.1016/j.neucom.2019.01.103","archive_url":"https://space-kitty.com/papers/arxiv:1807.07356","updated":"2019-01-31","pmid":"31595105","pmcid":null,"openalex_id":"W2887976372","pdf_url":"https://www.sciencedirect.com/science/article/pii/S0925231219301961/pdf","abstract":"Despite the state-of-the-art performance for medical image segmentation, deep convolutional neural networks (CNNs) have rarely provided uncertainty estimations regarding their segmentation outputs, e.g., model (epistemic) and image-based (aleatoric) uncertainties. In this work, we analyze these different types of uncertainties for CNN-based 2D and 3D medical image segmentation tasks. We additionally propose a test-time augmentation-based aleatoric uncertainty to analyze the effect of different transformations of the input image on the segmentation output. Test-time augmentation has been previously used to improve segmentation accuracy, yet not been formulated in a consistent mathematical framework. Hence, we also propose a theoretical formulation of test-time augmentation, where a distribution of the prediction is estimated by Monte Carlo simulation with prior distributions of parameters in an image acquisition model that involves image transformations and noise. We compare and combine our proposed aleatoric uncertainty with model uncertainty. Experiments with segmentation of fetal brains and brain tumors from 2D and 3D Magnetic Resonance Images (MRI) showed that 1) the test-time augmentation-based aleatoric uncertainty provides a better uncertainty estimation than calculating the test-time dropout-based model uncertainty alone and helps to reduce overconfident incorrect predictions, and 2) our test-time augmentation outperforms a single-prediction baseline and dropout-based multiple predictions.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":"cc-by","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/s21062222","kind":"article","title":"MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers","authors":["Jaeyong Kang","Zahid Ullah","Jeonghwan Gwak"],"author_count":3,"year":2021,"published":"2021-03-22","venue":"Sensors","cited_by":593,"doi":"10.3390/s21062222","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3390/s21062222","archive_url":"https://space-kitty.com/papers/doi:10.3390/s21062222","updated":null,"pmid":"33810176","pmcid":null,"openalex_id":"W3136979370","pdf_url":"https://www.mdpi.com/1424-8220/21/6/2222/pdf?version=1616574103","abstract":"Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Brain Tumor Detection and Classification"],"details":null,"provenance":"openalex","attribution":"Abstract of \"MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers\" by Jaeyong Kang, Zahid Ullah, Jeonghwan Gwak, https://doi.org/10.3390/s21062222, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41593-021-00962-x","kind":"article","title":"A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence","authors":["Emily Allen","Ghislain St-Yves","Yihan Wu","Jesse L. Breedlove","Jacob S. Prince","Logan Thorne Dowdle","Matthias Nau","Bradley A. Caron","Franco Pestilli","Ian Charest","J. Benjamin Hutchinson","Thomas Naselaris","Kendrick Kay"],"author_count":13,"year":2021,"published":"2021-12-16","venue":"Nature Neuroscience","cited_by":582,"doi":"10.1038/s41593-021-00962-x","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41593-021-00962-x","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41593-021-00962-x","updated":null,"pmid":"34916659","pmcid":null,"openalex_id":"W4200613736","pdf_url":"https://www.biorxiv.org/content/biorxiv/early/2021/02/22/2021.02.22.432340.full.pdf","abstract":null,"abstract_license":null,"license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Visual perception and processing mechanisms"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"arxiv:1801.05412","kind":"article","title":"An automated system for epilepsy detection using EEG brain signals based on deep learning approach","authors":["Ihsan Ullah","Muhammad Hussain","Emad-ul-Haq Qazi","Hatim Aboalsamh"],"author_count":4,"year":2018,"published":"2018-04-18","venue":"Expert Systems with Applications","cited_by":622,"doi":"10.1016/j.eswa.2018.04.021","arxiv_id":"1801.05412","nct_id":null,"source_url":"https://doi.org/10.1016/j.eswa.2018.04.021","archive_url":"https://space-kitty.com/papers/arxiv:1801.05412","updated":"2018-01-16","pmid":null,"pmcid":null,"openalex_id":"W2783678721","pdf_url":"https://arxiv.org/pdf/1801.05412","abstract":"Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists and affects their performance. Several automatic techniques have been proposed using traditional approaches to assist neurologists in detecting binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal. These methods do not perform well when classifying ternary case e.g. ictal vs. normal vs. inter-ictal; the maximum accuracy for this case by the state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a system based on deep learning, which is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model, the bottleneck is the large number of learnable parameters. P-1D-CNN works on the concept of refinement approach and it results in 60% fewer parameters compared to traditional CNN models. Further to overcome the limitations of small amount of data, we proposed augmentation schemes for learning P-1D-CNN model. In almost all the cases concerning epilepsy detection, the proposed system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":"cc-by-nc-nd","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["EEG and Brain-Computer Interfaces"],"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:2107.12979","kind":"preprint","title":"Predictive Coding: a Theoretical and Experimental Review","authors":["Beren Millidge","Anil Seth","Christopher L Buckley"],"author_count":3,"year":2021,"published":"2021-07-27","venue":null,"cited_by":106,"doi":null,"arxiv_id":"2107.12979","nct_id":null,"source_url":"https://arxiv.org/abs/2107.12979","archive_url":"https://space-kitty.com/papers/arxiv:2107.12979","updated":"2022-07-12","pmid":null,"pmcid":null,"openalex_id":"W3186883833","pdf_url":"https://arxiv.org/pdf/2107.12979","abstract":"Predictive coding offers a potentially unifying account of cortical function -- postulating that the core function of the brain is to minimize prediction errors with respect to a generative model of the world. The theory is closely related to the Bayesian brain framework and, over the last two decades, has gained substantial influence in the fields of theoretical and cognitive neuroscience. A large body of research has arisen based on both empirically testing improved and extended theoretical and mathematical models of predictive coding, as well as in evaluating their potential biological plausibility for implementation in the brain and the concrete neurophysiological and psychological predictions made by the theory. Despite this enduring popularity, however, no comprehensive review of predictive coding theory, and especially of recent developments in this field, exists. Here, we provide a comprehensive review both of the core mathematical structure and logic of predictive coding, thus complementing recent tutorials in the literature. We also review a wide range of classic and recent work within the framework, ranging from the neurobiologically realistic microcircuits that could implement predictive coding, to the close relationship between predictive coding and the widely-used backpropagation of error algorithm, as well as surveying the close relationships between predictive coding and modern machine learning techniques.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"retracted":false,"peer_reviewed":false,"categories":["cs.AI","cs.NE","q-bio.NC"],"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.clinph.2022.04.022","kind":"article","title":"Transcranial magnetic stimulation of the brain: What is stimulated? – A consensus and critical position paper","authors":["Hartwig Roman Siebner","Klaus Funke","Aman S. Aberra","Andrea Antal","Sven Bestmann","Robert E. Chen","Joseph Claßen","Marco Davare","Vincenzo Di Lazzaro","Peter T. Fox","Mark Hallett","Anke Ninija Karabanov","Janine Kesselheim","Mikkel Malling Beck","Giacomo Koch","David Liebetanz","Sabine Meunier","Carlo Miniussi","Walter Paulus","Angel V. Peterchev","Traian Popa","Michael C. Ridding","Axel Thielscher","Ulf Ziemann","John C. Rothwell"],"author_count":26,"year":2022,"published":"2022-05-18","venue":"Clinical Neurophysiology","cited_by":566,"doi":"10.1016/j.clinph.2022.04.022","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.clinph.2022.04.022","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.clinph.2022.04.022","updated":null,"pmid":"35738037","pmcid":null,"openalex_id":"W4280629063","pdf_url":"https://www.sciencedirect.com/science/article/pii/S1388245722002723/pdf","abstract":"Transcranial (electro)magnetic stimulation (TMS) is currently the method of choice to non-invasively induce neural activity in the human brain. A single transcranial stimulus induces a time-varying electric field in the brain that may evoke action potentials in cortical neurons. The spatial relationship between the locally induced electric field and the stimulated neurons determines axonal depolarization. The induced electric field is influenced by the conductive properties of the tissue compartments and is strongest in the superficial parts of the targeted cortical gyri and underlying white matter. TMS likely targets axons of both excitatory and inhibitory neurons. The propensity of individual axons to fire an action potential in response to TMS depends on their geometry, myelination and spatial relation to the imposed electric field and the physiological state of the neuron. The latter is determined by its transsynaptic dendritic and somatic inputs, intrinsic membrane potential and firing rate. Modeling work suggests that the primary target of TMS is axonal terminals in the crown top and lip regions of cortical gyri. The induced electric field may additionally excite bends of myelinated axons in the juxtacortical white matter below the gyral crown. Neuronal excitation spreads ortho- and antidromically along the stimulated axons and causes secondary excitation of connected neuronal populations within local intracortical microcircuits in the target area. Axonal and transsynaptic spread of excitation also occurs along cortico-cortical and cortico-subcortical connections, impacting on neuronal activity in the targeted network. Both local and remote neural excitation depend critically on the functional state of the stimulated target area and network. TMS also causes substantial direct co-stimulation of the peripheral nervous system. Peripheral co-excitation propagates centrally in auditory and somatosensory networks, but also produces brain responses in other networks subserving multisensory integration, orienting or arousal. The complexity of the response to TMS warrants cautious interpretation of its physiological and behavioural consequences, and a deeper understanding of the mechanistic underpinnings of TMS will be critical for advancing it as a scientific and therapeutic tool.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Transcranial Magnetic Stimulation Studies"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Transcranial magnetic stimulation of the brain: What is stimulated? – A consensus and critical position paper\" by Hartwig Roman Siebner, Klaus Funke, Aman S. Aberra et al., https://doi.org/10.1016/j.clinph.2022.04.022, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1016/j.bbe.2018.10.004","kind":"article","title":"Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms","authors":["Amin Kabir Anaraki","Moosa Ayati","Foad Kazemi"],"author_count":3,"year":2018,"published":"2018-10-18","venue":"Journal of Applied Biomedicine","cited_by":611,"doi":"10.1016/j.bbe.2018.10.004","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.bbe.2018.10.004","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.bbe.2018.10.004","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2897188827","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Brain Tumor Detection and Classification"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."}],"next":"https://space-kitty.com/api/v1/topics/neuroai/papers?sort=top&limit=25&offset=25"}