Space Kitty Archive

NeuroAI & Computational Neuroscience

Neural networks as models of the brain, predictive coding, neural population dynamics and foundation models trained on neural data.

227 papersnewest Oct 27, 2026

Most influential

  • Brain tumor segmentation with Deep Neural Networks
    Mohammad Havaei, Axel Davy, David Warde-Farley et al. · 2016 · Medical Image Analysis

    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…

    Article3,277 citationsarXiv:1505.03540doi:10.1016/j.media.2016.05.004
  • Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
    Sérgio Pereira, Adriano B. Pinto, Victor Alves et al. · 2016 · IEEE Transactions on Medical Imaging
    Article2,735 citationsdoi:10.1109/tmi.2016.2538465
  • Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing
    Nikolaus Kriegeskorte · 2015 · Annual Review of Vision Science
    Review1,083 citationsdoi:10.1146/annurev-vision-082114-0354…
  • Multi-grade brain tumor classification using deep CNN with extensive data augmentation
    Muhammad Sajjad, Salman Khan, Khan Muhammad et al. · 2018 · Journal of Computational Science
    Article895 citationsdoi:10.1016/j.jocs.2018.12.003
  • Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker
    James Howard Cole, Rudra P. K. Poudel, Dimosthenis Tsagkrasoulis et al. · 2017 · NeuroImage

    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 …

    Article870 citationsarXiv:1612.02572doi:10.1016/j.neuroimage.2017.07.059
  • Brain tumor classification for MR images using transfer learning and fine-tuning
    Zar Nawab Khan Swati, Qinghua Zhao, Muhammad Kabir et al. · 2019 · Computerized Medical Imaging and Graphics
    Article843 citationsdoi:10.1016/j.compmedimag.2019.05.001
  • MR-based synthetic CT generation using a deep convolutional neural network method
    Xiao Han · 2017 · Medical Physics

    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 p…

    Article868 citationsarXiv:1704.07239doi:10.1002/mp.12155
  • Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation
    Junhao Wen, Elina Thibeau–Sutre, Mauricio Díaz et al. · 2020 · Medical Image Analysis

    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 the…

    Review801 citationsarXiv:1904.07773doi:10.1016/j.media.2020.101694
  • Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
    Charles F. Cadieu, Ha Hong, Daniel L. K. Yamins et al. · 2014 · PLoS Computational Biology

    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, r…

    Article824 citationsarXiv:1406.3284doi:10.1371/journal.pcbi.1003963
  • Deep convolutional neural networks for multi-modality isointense infant brain image segmentation
    Wenlu Zhang, Rongjian Li, Houtao Deng et al. · 2015 · NeuroImage
    Article817 citationsdoi:10.1016/j.neuroimage.2014.12.061
  • A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
    Xiaomei Zhao, Yihong Wu, Guidong Song et al. · 2017 · Medical Image Analysis

    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 fram…

    Article813 citationsarXiv:1702.04528doi:10.1016/j.media.2017.10.002
  • Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation
    Michael Yeung, Evis Sala, Carola‐Bibiane Schönlieb et al. · 2021 · Computerized Medical Imaging and Graphics

    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…

    Abstract excerpt by Michael Yeung et al., CC-BY, https://doi.org/10.1016/j.compmedimag.2021.102026; truncated.

    Article748 citationsdoi:10.1016/j.compmedimag.2021.102026
  • Multi-Classification of Brain Tumor Images Using Deep Neural Network
    Hossam H. Sultan, Nancy M. Salem, Walid Al‐Atabany · 2019 · IEEE Access
    Article774 citationsdoi:10.1109/access.2019.2919122
  • Multimodal deep learning models for early detection of Alzheimer’s disease stage
    Janani Venugopalan, Tong Li, Hamid Reza Hassanzadeh et al. · 2021 · Scientific Reports

    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…

    Abstract excerpt by Janani Venugopalan et al., CC-BY, https://doi.org/10.1038/s41598-020-74399-w; truncated.

    Article732 citationsdoi:10.1038/s41598-020-74399-w
  • Deep neural networks are more accurate than humans at detecting sexual orientation from facial images.
    Yilun Wang, Michał Kosiński · 2018 · Journal of Personality and Social Psychology

    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…

    Abstract excerpt by Yilun Wang et al., CC-BY, https://doi.org/10.1037/pspa0000098; truncated.

    Article766 citationsdoi:10.1037/pspa0000098

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