Manual annotation of the images of thin tissue sections remains a time-consuming step in Mueller microscopy and limits its scalability. We present a novel automated approach using only the total intensity M11 element of the Mueller matrix as an input to a U-Net architecture with a pretrained ResNet-34 encoder. The network was trained to distinguish four classes in the images of murine uterine cervix sections: background, internal os, cervical tissue, and vaginal wall. With only 70 cervical tissue sections, the model achieved 89.71% pixel accuracy and 80.96% mean tissue Dice coefficient on the held-out test dataset. Transfer learning from ImageNet enables accurate segmentation despite limited size of training dataset typical of specialized biomedical imaging. This intensity-based framework requires minimal preprocessing and is readily extensible to other imaging modalities and tissue types, with publicly available graphical annotation tools for practical deployment.
ResNet-34 with Lightweight Decoder for Accurate and Efficient Segmentation of Fetal Brain MRI
Department of Biomedical Physics and Technology, University of Dhaka（达卡大学生物医学物理与技术系）；Department of Electrical Engineering, College of Engineering, Qatar University（卡塔尔大学工程学院电气工程系）；Department of Biomedical Engineering, Jashore University of Science and Technology（贾沙尔大学科学与技术学院生物医学工程系）
University of Bergen（卑尔根大学）；SimulaMet（西穆拉梅特）；Nepal Medical College（尼泊尔医学院）；Kathmandu University（加德满都大学）；UiT The Arctic University of Norway（挪威北极大学）；University of Oslo（奥斯陆大学）；Oslo Metropolitan University（奥斯陆城市大学）
Deep learning and classical computer vision techniques in medical image analysis: Case studies on brain MRI tissue segmentation, lung CT COPD registration, and skin lesion classification
University of Exeter（埃克塞特大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Sorbonne Université（索邦大学）
A superpixel-driven deep learning approach for the analysis of dermatological wounds
Institute of Mathematics；Computer Sciences -- ICMC/USP -- Brazil a Ribeir\ a o Preto Medical School - HCFMRP/USP -- Brazil b；Department of Physical Therapy -- DFisio/UFSCar -- Brazil c；Institute of Computing -- IC/UFF -- Brazil d Fluminense Northwest Institute -- INFES/UFF -- Brazil e
Classification of Brain Tumours in MR Images using Deep Spatiospatial Models
Otto von Guericke University Magdeburg（奥托·冯·格里克马格德堡大学）；Center for Behavioral Brain Sciences（行为脑科学中心）；German Center for Neurodegenerative Disease（德国神经退行性疾病中心）；Leibniz Institute for Neurobiology（莱布尼茨神经生物学研究所）
BreastSegNet: Multi-label Segmentation of Breast MRI
Department of Biostatistics \& Bioinformatics, Duke University Department of Electrical；Computer Engineering, Duke University Department of Radiology, Duke University Medical Center Department of Computer Science, Duke University
Weakly Supervised Segmentation and Classification of Alpha-Synuclein Aggregates in Brightfield Midbrain Images
Computational Biology, Ecole Normale Sup\'erieure, PSL University, Paris, France. 2 AP-HP, Hôpital Henri Mondor-Albert Chenevier, Service de Neurologie, F-94010 Créteil, France. 3 INSERM U955, Institut Mondor de Recherche Biomédicale, UPEC, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France. 4 Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France. 5 NeurATRIS, Créteil, France. 6 Nuffield Department of Clinical Neurosciences；the Queen’s College, University of Oxford, UK. 7 Department of Women’s；Children’s Health, Karolinska Institutet, Sweden. 8 Churchill College, Cambridge University, CB30DS UK.
Enhancing Prostate Cancer Diagnosis with Deep Learning: A Study using mpMRI Segmentation and Classification
KLS Gogte Institute of Technology（KLS戈格特理工学院）；University of Stavanger（斯塔万格大学）；Dr. M.S. Sheshgiri College of Engineering and Technology（M.S.谢什吉里博士工程技术学院）；JN Medical College（JN医学院）
MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images
University of Leicester（莱斯特大学）；NIHR Leicester Biomedical Research Centre（NIHR莱斯特生物医学研究中心）；Glenfield Hospital（格伦菲尔德医院）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）；Saint-Petersburg State Electrotechnical University（圣彼得堡国立电工大学）
