Delineation of cancerous regions in gigapixel whole slide images (WSIs) is a crucial diagnostic procedure in digital pathology. This process is time-consuming because of the large search space in the gigapixel WSIs, causing chances of omission and misinterpretation at indistinct tumor lesions. To tackle this, the development of an automated cancerous region segmentation method is imperative. We frame this issue as a modeling problem with partial label WSIs, where some cancerous regions may be misclassified as benign and vice versa, producing patches with noisy labels. To learn from these patches, we propose Self-similarity Student, combining teacher-student model paradigm with similarity learning. Specifically, for each patch, we first sample its similar and dissimilar patches according to spatial distance. A teacher-student model is then introduced, featuring the exponential moving average on both student model weights and teacher predictions ensemble. While our student model takes patches, teacher model takes all their corresponding similar and dissimilar patches for learning robust representation against noisy label patches. Following this similarity learning, our similarity ensemble merges similar patches' ensembled predictions as the pseudo-label of a given patch to counteract its noisy label. On the CAMELYON16 dataset, our method substantially outperforms state-of-the-art noise-aware learning methods by 5$\%$ and the supervised-trained baseline by 10$\%$ in various degrees of noise. Moreover, our method is superior to the baseline on our TVGH TURP dataset with 2$\%$ improvement, demonstrating the generalizability to more clinical histopathology segmentation tasks.
Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation
Northwestern Polytechnical University（西北工业大学）；La Trobe University（拉筹伯大学）；CSIRO Data61（联邦科学与工业研究组织数据61部）；University of New South Wales（新南威尔士大学）；Shandong University（山东大学）；Tianjin University（天津大学）
Semi-supervised Segmentation of Histopathology Images with Noise-Aware Topological Consistency
Stony Brook University（石溪大学）；Athinoula A. Martinos Center for Biomedical Imaging（阿西努拉·A.马蒂诺斯生物医学成像中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Can We Go Beyond Visual Features? Neural Tissue Relation Modeling for Relational Graph Analysis in Non-Melanoma Skin Histology
Mohamed bin Zayed University of AI（Mohamed bin Zayed人工智能大学）；School of Information and Data Sciences（信息与数据科学学院）；Vellore Institute of Technology（维洛雷理工学院）；Loughborough University（洛桑大学）；MD Anderson Cancer Center, The University of Texas（MD安德森癌症中心，德克萨斯大学）
The Pennsylvania State University（宾夕法尼亚州立大学）；Lister Hill National Center for Biomedical Communications（利斯特希尔国家生物医学通信中心）；National Library of Medicine（国家医学图书馆）；Penn State Health Milton S. Hershey Medical Center（宾夕法尼亚州立大学健康米尔顿·S·赫尔希医学中心）；Penn State College Of Medicine（宾夕法尼亚州立大学医学院）
Semi-supervised Semantic Segmentation for Remote Sensing Images via Multi-scale Uncertainty Consistency and Cross-Teacher-Student Attention
Faculty of Data Science, City University of Macau（数据科学学院，澳门城市大学）；WMG, University of Warwick（沃里克大学工程学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航天信息研究所）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子、电气与通信工程学院）；Department of Automation, Tsinghua University（清华大学自动化系）
Henan University of Chinese Medicine（河南中医药大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency
Stony Brook University（石英溪大学）；Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School（阿提诺拉A.马丁努斯生物医学影像中心，麻省总医院和哈佛医学院）；Department of Biomedical Data Science, Stanford University（生物医学数据科学系，斯坦福大学）；Department of Pathology and Cell Biology, Columbia University（病理学与细胞生物学系，哥伦比亚大学）
