通知：联邦节假日调休：哥伦布日 / 原住民日（Columbus Day / Indigenous Peoples' Day），今日（13日）无更新。通知：联邦节假日调休：哥伦布日 / 原住民日（Columbus Day / Indigenous Peoples' Day），今日（13日）无更新。
arXiv 2007.00311cs.CV
Towards Explainable Graph Representations in Digital Pathology
EPFL（洛桑联邦理工学院）；IBM Research Zürich（IBM苏黎世研究院）；ETH Zürich（苏黎世联邦理工学院）；National Cancer Institute - IRCCS-Fondazione Pascale（国家癌症研究所 - IRCCS帕斯卡尔基金会）
Guillaume Jaume, Pushpak Pati, Antonio Foncubierta-Rodriguez, Florinda Feroce, Giosue Scognamiglio, Anna Maria Anniciello, Jean-Philippe Thiran, Orcun Goksel, Maria Gabrani
Explainability of machine learning (ML) techniques in digital pathology (DP) is of great significance to facilitate their wide adoption in clinics. Recently, graph techniques encoding relevant biological entities have been employed to represent and assess DP images. Such paradigm shift from pixel-wise to entity-wise analysis provides more control over concept representation. In this paper, we introduce a post-hoc explainer to derive compact per-instance explanations emphasizing diagnostically important entities in the graph. Although we focus our analyses to cells and cellular interactions in breast cancer subtyping, the proposed explainer is generic enough to be extended to other topological representations in DP. Qualitative and quantitative analyses demonstrate the efficacy of the explainer in generating comprehensive and compact explanations.
Kharkevich Institute for Information Transmission Problems（信息传输问题研究所（Kharkevich研究所））；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）
Explaining Digital Pathology Models via Clustering Activations
Faculty of Informatics, Masaryk University, Brno, Czech Republic（马萨里克大学信息学院，布拉格，捷克共和国）；Institute of Computer Science, Masaryk University, Brno, Czech Republic（马萨里克大学计算机科学研究所，布拉格，捷克共和国）；Masaryk Memorial Cancer Institute, Brno, Czech Republic（马萨里克纪念癌症研究所，布拉格，捷克共和国）
Discrepancy Minimization Improves Cross-Hospital Robustness in Digital Pathology
Reichman University, Herzliya, Israel（以色列海法大学）；Institute of Pathology, Sheba Medical Center, Ramat-Gan, Israel（以色列沙巴医疗中心病理研究所）；Technion - Israel Institute of Technology, Haifa, Israel（以色列技术学院）
Stony Brook University（石溪大学）；Brigham and Women's Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Fraunhofer Institute for Medical Image Computing MEVIS（弗劳恩霍夫医学图像计算研究所）；University of Bremen（不来梅大学）
Foundation Models for Slide-level Cancer Subtyping in Digital Pathology
Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano (HUMAN-Tech)（以人为本技术大学研究院（HUMAN-Tech））；Universitat Politécnica de Valencia（瓦伦西亚理工大学）；Valencian Graduate School and Research Network for Artificial Intelligence (valgraAI)（瓦伦西亚人工智能研究生院与研究网络（valgraAI））
Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology
Department of Computer Science Vanderbilt University, TN, USA（计算机科学系范德堡大学）；Department of Radiology Weill Cornell Medicine, NY, USA（放射学系韦尔·科恩医学中心）；Department of Medicine Vanderbilt University Medical Center, TN, USA Veterans Affairs Tennessee Valley Healthcare System TN, USA（医学系范德堡大学医学中心退伍军人事务田纳西山谷医疗系统）；Department of Medicine Vanderbilt University Medical Center Veterans Affairs Tennessee Valley Healthcare System TN, USA（医学系范德堡大学医学中心退伍军人事务田纳西山谷医疗系统）；Department of Anatomical Pathology UT MD Anderson Cancer Center,TX, USA（解剖病理学系UT MD安德森癌症中心）
