Progress in digital pathology is hindered by high-resolution images and the prohibitive cost of exhaustive localized annotations. The commonly used paradigm to categorize pathology images is patch-based processing, which often incorporates multiple instance learning (MIL) to aggregate local patch-level representations yielding image-level prediction. Nonetheless, diagnostically relevant regions may only take a small fraction of the whole tissue, and current MIL-based approaches often process images uniformly, discarding the inter-patches interactions. To alleviate these issues, we propose ScoreNet, a new efficient transformer that exploits a differentiable recommendation stage to extract discriminative image regions and dedicate computational resources accordingly. The proposed transformer leverages the local and global attention of a few dynamically recommended high-resolution regions at an efficient computational cost. We further introduce a novel mixing data-augmentation, namely ScoreMix, by leveraging the image's semantic distribution to guide the data mixing and produce coherent sample-label pairs. ScoreMix is embarrassingly simple and mitigates the pitfalls of previous augmentations, which assume a uniform semantic distribution and risk mislabeling the samples. Thorough experiments and ablation studies on three breast cancer histology datasets of Haematoxylin & Eosin (H&E) have validated the superiority of our approach over prior arts, including transformer-based models on tumour regions-of-interest (TRoIs) classification. ScoreNet equipped with proposed ScoreMix augmentation demonstrates better generalization capabilities and achieves new state-of-the-art (SOTA) results with only 50% of the data compared to other mixing augmentation variants. Finally, ScoreNet yields high efficacy and outperforms SOTA efficient transformers, namely TransPath and SwinTransformer.
Towards Histopathological Stain Invariance by Unsupervised Domain Augmentation using Generative Adversarial Networks
University of Strasbourg（斯特拉斯堡大学）；University of Belgrade（贝尔格莱德大学）；University of Kragujevac（克拉古耶瓦茨大学）；Hannover Medical School（汉诺威医学院）；University Clinic, Freiburg（弗赖堡大学诊所）
Test Time Transform Prediction for Open Set Histopathological Image Recognition
Universitat Pompeu Fabra（庞培法布拉大学）；University of Adelaide（阿德莱德大学）；University Hospital RWTH Aachen（亚琛工业大学医院）；Catalan Institution for Research and Advanced Studies (ICREA)（加泰罗尼亚研究与高级研究院（ICREA））
Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification
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）
Prediction of lung and colon cancer through analysis of histopathological images by utilizing Pre-trained CNN models with visualization of class activation and saliency maps
Jaypee University of Information Technology（贾皮尔信息技术大学）
MorphGen: Morphology-Guided Representation Learning for Robust Single-Domain Generalization in Histopathological Cancer Classification
Department of Pathology, College of Medicine, The Ohio State University Wexner Medical Center（病理学系，医学学院，俄亥俄州立大学韦克斯纳医学中心）；Department of Computer Science and Engineering, Chung-Ang University（计算机科学与工程系，成均馆大学）；School of Computer Science and Engineering, Central South University（计算机科学与工程学院，中南大学）；College of Medicine Rockford, National Center for Rural Health Professions, University of Illinois（医学学院Rockford，农村健康专业中心，伊利诺伊大学）；Institute of Computing, Kohat University of Science and Technology（计算学院，科阿特大学科技学院）；Department of Imaging Physics, The University of Texas MD Anderson Cancer Center（成像物理系，德克萨斯大学MD安德森癌症中心）
Towards Global AI-Driven Cervical Cancer Screening
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；National Center for Tumor Diseases (NCT) Heidelberg（海德堡国家肿瘤疾病中心）；Helmholtz Association（亥姆霍兹联合会）；University of Heidelberg（海德堡大学）；Medical Faculty Heidelberg（海德堡医学院）；University Hospital Heidelberg（海德堡大学医院）；German Consortium for Translational Cancer Research (DKTK)（德国转化癌症研究联盟）；National Center for Tumor Diseases (NCT) Dresden（德累斯顿国家肿瘤疾病中心）；University Hospital Carl Gustav Carus Dresden（德累斯顿卡尔·古斯塔夫·卡鲁斯大学医院）；Technische Universität Dresden（德累斯顿工业大学）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（亥姆霍兹德累斯顿罗森多夫研究中心）；University of Bonn（波恩大学）；University Hospital Bonn（波恩大学医院）；University of Cologne（科隆大学）；University Hospital Cologne（科隆大学医院）；University of Duisburg-Essen（杜伊斯堡-埃森大学）；University Hospital Essen（埃森大学医院）；University of Freiburg（弗莱堡大学）；University Hospital Freiburg（弗莱堡大学医院）；University of Göttingen（哥廷根大学）；University Hospital Göttingen（哥廷根大学医院）；University of Hamburg（汉堡大学）；University Hospital Hamburg-Eppendorf（汉堡-埃彭多夫大学医院）；University of Jena（耶拿大学）；University Hospital Jena（耶拿大学医院）；University of Kiel（基尔大学）；University Hospital Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医院）；University of Leipzig（莱比锡大学）；University Hospital Leipzig（莱比锡大学医院）；University of Lübeck（吕贝克大学）；University Hospital Lübeck（吕贝克大学医院）；University of Magdeburg（马格德堡大学）；University Hospital Magdeburg（马格德堡大学医院）；University of Mainz（美因茨大学）；University Hospital Mainz（美因茨大学医院）；University of Marburg（马尔堡大学）；University Hospital Marburg（马尔堡大学医院）；University of Munich (LMU)（慕尼黑大学）；University Hospital Munich (LMU)（慕尼黑大学医院）；Technical University of Munich (TUM)（慕尼黑工业大学）；University Hospital rechts der Isar (TUM)（慕尼黑工业大学伊萨尔河右岸医院）；University of Münster（明斯特大学）；University Hospital Münster（明斯特大学医院）；University of Regensburg（雷根斯堡大学）；University Hospital Regensburg（雷根斯堡大学医院）；University of Rostock（罗斯托克大学）；University Hospital Rostock（罗斯托克大学医院）；University of Tübingen（蒂宾根大学）；University Hospital Tübingen（蒂宾根大学医院）；University of Ulm（乌尔姆大学）；University Hospital Ulm（乌尔姆大学医院）；University of Würzburg（维尔茨堡大学）；University Hospital Würzburg（维尔茨堡大学医院）
