Histopathology image classification is crucial for the accurate identification and diagnosis of various diseases but requires large and diverse datasets. Obtaining such datasets, however, is often costly and time-consuming due to the need for expert annotations and ethical constraints. To address this, we examine the suitability of different generative models and image selection approaches to create realistic synthetic histopathology image patches conditioned on class labels. Our findings highlight the importance of selecting an appropriate generative model type and architecture to enhance performance. Our experiments over the PCam dataset show that diffusion models are effective for transfer learning, while GAN-generated samples are better suited for augmentation. Additionally, transformer-based generative models do not require image filtering, in contrast to those derived from Convolutional Neural Networks (CNNs), which benefit from realism score-based selection. Therefore, we show that synthetic images can effectively augment existing datasets, ultimately improving the performance of the downstream histopathology image classification task.
DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology
University of Glasgow（格拉斯哥大学）；Dotphoton；Fraunhofer HHI（弗劳恩霍夫海因里希·赫兹研究所）；Aignostics；Institute of Pathology, LMU Munich（慕尼黑大学病理学研究所）；UC Berkeley（加州大学伯克利分校）；TU Berlin（柏林工业大学）
Dataset Distillation for Histopathology Image Classification
Australian Institute of Health Innovation (AIHI), Macquarie University（澳大利亚麦考瑞大学澳大利亚健康创新研究所）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Computer Science, Peking University（北京大学计算机学院）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）
Stony Brook University（石溪大学）；Argonne National Laboratory（阿贡国家实验室）；University of Chicago（芝加哥大学）；The University of Utah（犹他大学）；The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
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）
DISC: Latent Diffusion Models with Self-Distillation from Separated Conditions for Prostate Cancer Grading
Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；The Catholic University of Korea College of Medicine（韩国天主教大学医学院）；Case Western Reserve University（凯斯西储大学）；Huntsman Cancer Institute, University of Utah Health（犹他大学健康亨茨曼癌症研究所）；University of Utah（犹他大学）
NephroNet: A Novel Program for Identifying Renal Cell Carcinoma and Generating Synthetic Training Images with Convolutional Neural Networks and Diffusion Models
GenPrior: Unleashing Text-to-Motion Generative Priors for Zero-Shot Skeleton-based Action Recognition
School of Cyber Science and Engineering, Southeast University（东南大学网络空间安全学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Purple Mountain Laboratories（紫金山实验室）
Between Generating Noise and Generating Images: Noise in the Correct Frequency Improves the Quality of Synthetic Histopathology Images for Digital Pathology
Technion - IIT（以色列理工学院）；Rambam Health Care Campus（兰巴姆医疗中心）
A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images
University of British Columbia（不列颠哥伦比亚大学）；Eindhoven University of Technology（埃因霍温理工大学）；Massachusetts General Hospital（马萨诸塞州综合医院）；Harvard Medical School（哈佛医学院）；BC Cancer Agency（不列颠哥伦比亚癌症机构）
SYNTA: A novel approach for deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data
MIRA Vision Microscopy GmbH（MIRA Vision 显微镜有限公司）；Friedrich-Alexander University Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Universitätsklinikum Erlangen（埃尔朗根大学医院）；German Aerospace Center (DLR)（德国航空航天中心）；University of Cologne（科隆大学）；Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V.（莱布尼茨分析科学研究所）
本文提出 SYNTA 方法，通过生成照片级逼真的合成生物医学图像作为深度学习训练数据，在无需人工标注的情况下实现了对真实肌肉组织切片的专家级分割，为 GAN 提供了可解释且可控的替代方案。
MeDi: Metadata-Guided Diffusion Models for Mitigating Biases in Tumor Classification
Machine Learning Group, Technische Universität Berlin, Germany The Konrad Zuse School of Excellence in Learning；Intelligent Systems (ELIZA), Germany BIFOLD – Berlin Institute for the Foundations of Learning
Diffusion-based Data Augmentation for Nuclei Image Segmentation
Shenzhen Research Institute of Big Data（深圳市大数据研究院）；School of Computer Science and Engineering, Research Institute of Sun Yat-sen University in Shenzhen, Sun Yat-sen University（中山大学计算机科学与工程学院，中山大学深圳研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Health Development Research and Data Management Center（深圳市卫生健康发展研究和数据管理中心）
Hard Sample Aware Noise Robust Learning for Histopathology Image Classification
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Beijing Chaoyang Hospital, Capital Medical University（首都医科大学附属北京朝阳医院）
Controllable Diffusion-Based Lesion Inpainting for Scalable Histopathology Data Augmentation
Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong（香港大学李嘉诚医学院诊断放射学系）；Department of Pathology, Li Ka Shing Faculty of Medicine, The University of Hong Kong（香港大学李嘉诚医学院病理学系）；Department of Anatomical and Cellular Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong（香港中文大学威尔斯亲王医院解剖及细胞病理学系）；Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong（香港城市大学赛马会动物医学及生命科学院传染病及公共卫生学系）；CityU Veterinary Diagnostic Laboratory Co., Ltd., City University of Hong Kong（香港城市大学城市大学兽医诊断实验室有限公司）
