Backpropagation-based supervised learning has achieved great success in computer vision tasks. However, its biological plausibility is always controversial. Recently, the bio-inspired Hebbian learning rule (HLR) has received extensive attention. Self-Organizing Map (SOM) uses the competitive HLR to establish connections between neurons, obtaining visual features in an unsupervised way. Although the representation of SOM neurons shows some brain-like characteristics, it is still quite different from the neuron representation in the human visual cortex. This paper proposes an improved SOM with multi-winner, multi-code, and local receptive field, named mlSOM. We observe that the neuron representation of mlSOM is similar to the human visual cortex. Furthermore, mlSOM shows a sparse distributed representation of objects, which has also been found in the human inferior temporal area. In addition, experiments show that mlSOM achieves better classification accuracy than the original SOM and other state-of-the-art HLR-based methods. The code is accessible at https://github.com/JiaHongZ/mlSOM.
The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons
Harvard Medical School（哈佛医学院）；Northeastern University（东北大学）；Harvard University（哈佛大学）；Boston College（波士顿学院）；MIT（麻省理工学院）；ESRF, The European Synchrotron（欧洲同步辐射装置）；Boston Children’s Hospital（波士顿儿童医院）
Brain Extraction from Normal and Pathological Images: A Joint PCA/Image-Reconstruction Approach
Department of Computer Science, University of North Carolina at Chapel Hill, USA（北卡罗来纳大学教堂山分校计算机科学系）；Department of Computer Science, University of Salzburg, Austria（萨尔茨堡大学计算机科学系）；Kitware Inc., USA（Kitware公司）；Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, USA（宾夕法尼亚大学佩雷尔曼医学院生物医学图像计算与分析中心）；Department of Computer Science, Technical University of Munich, Germany（慕尼黑工业大学计算机科学系）；Institute of Neuroimaging and Informatics, University of Southern California, USA（南加州大学神经影像与信息学研究所）；David Geffen School of Medicine, UCLA Medical Center, USA（加州大学洛杉矶分校医学中心大卫·格芬医学院）
Sharing deep generative representation for perceived image reconstruction from human brain activity
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Center for Excellence in Brain Science and Intelligence Technology, CAS（中国科学院脑科学与智能技术卓越创新中心）
Data-driven registration and modeling of brain deformation for image-guided neurosurgery
LASIGE, Faculty of Sciences, University of Lisbon（里斯本大学科学学院LASIGE）；Department of Neurosurgery and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School（哈佛医学院布里洛妇女医院神经外科与放射科）；Gina Cody School of Engineering and Computer Science, Concordia University（康科迪亚大学工程与计算机科学学院）；Cancer Research UK Cambridge Centre, University of Cambridge（剑桥大学癌症研究英国中心）；Department of Oncology, University of Cambridge（剑桥大学肿瘤科）；Department of Clinical Neurosciences, University of Cambridge（剑桥大学临床神经科学系）；Computational Health Informatics Program (CHIP) and Department of Radiology, Boston Children's Hospital, Harvard Medical School（哈佛医学院波士顿儿童医院计算健康信息学计划与放射科）；Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM（索邦大学巴黎脑研究所-ICM）
Multi-Modal Brain Tumor Segmentation via 3D Multi-Scale Self-attention and Cross-attention
University of Electronic Science and Technology of China（电子科技大学）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）
