Histopathology Image Synthesis

Latest papers

8 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Agent-Driven Generative Priors for Pathology Image Synthesis and Segmentation
Nov 4, 2026Sicheng Yang, Rui Tang, Haipeng Zhou +6Histopathology Image Synthesis Computational Pathology
Sicheng Yang, Haipeng Zhou, Hongqiu Wang, and Zhaohu Xing are with the Hong Kong University of Science and Technology (Guangzhou), China. · Rui Tang is with The University of Hong Kong, Pokfulam, Hong Kong SAR, China. · Jianda Ma and Lie Dai are with Department of Rheumatology and Immunology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, China. +3
BigCell: Generating Gigapixel Whole-Slide Images
Oct 9, 2026Srikar Yellapragada, Alexandros Graikos, Zilinghan Li +10Histopathology Image Synthesis Image Generation
Stony Brook University · Argonne National Laboratory · University of Chicago +2
Controllable Histopathology Image Synthesis with Training-free Structural Initialization and Textural Modulation
Jun 26, 2026Yuheng Qiu, Jingyi Luo, Chenfei Ye +2Diffusion Model Guidance Histopathology Image Synthesis
School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China
STREAM: Stochastic Riemannian Flow Matching with Anisotropic Decoder for Digital Histopathology Image Generation
Jun 5, 2026Won June Cho, Daeky Jeong, Hyeongyeol Lim +1Flow Matching Diffusion Transformer
DEEPNOID Inc.
PathAR: Structure-First Autoregressive Synthesis of Multimodal Pathology Images
Jun 1, 2026Yuan Zhang, Jiahao Xia, Junzhang Huang +4Conditional Image Generation Histopathology Image Synthesis
Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing 210096, China · Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore · Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore +2
CytoSyn: a Foundation Diffusion Model for Histopathology -- Tech Report
Mar 18, 2026Thomas Duboudin, Xavier Fontaine, Etienne Andrier +7Histopathology Image Synthesis Medical Image Synthesis
Owkin, Inc
Structure-Guided Histopathology Synthesis via Dual-LoRA Diffusion
Mar 4, 2026Xuan Xu, Prateek PrasannaHistopathology Image Synthesis Medical Image Synthesis
Stony Brook University
Controllable Diffusion-Based Lesion Inpainting for Scalable Histopathology Data Augmentation
Jan 13, 2026Mohamad Koohi-Moghadam, Mohammad-Ali Nikouei Mahani, Rex K. H. Au-Yeung +6Computational Pathology Synthetic Data Augmentation
Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, PR China · Department of Pathology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China · Department of Anatomical and Cellular Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China. +2

Nov 4, 2026J/K move · Enter open · S save

Agent-Driven Generative Priors for Pathology Image Synthesis and Segmentation

Sicheng Yang, Rui Tang, Haipeng Zhou, Hongqiu Wang+5

Sicheng Yang, Haipeng Zhou, Hongqiu Wang, and Zhaohu Xing are with the Hong Kong University of Science and Technology (Guangzhou), China. · Rui Tang is with The University of Hong Kong, Pokfulam, Hong Kong SAR, China. · Jianda Ma and Lie Dai are with Department of Rheumatology and Immunology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, China.+3

Histopathology Image Synthesis Computational Pathology Histopathology Image Segmentation Synthetic Data Augmentation Medical Image Synthesis Medical Image Representation Learning

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Deep learning methods for pathology are constrained by the limited availability and high cost of large-scale pixel-level annotations. Synthetic data can alleviate this limitation, but existing pipelines often lack reliable quality control and remain weakly connected to downstream analysis. To address these issues, we propose a three-stage framework that integrates automated data screening, generative representation learning, and efficient adaptation to downstream tasks. First, we develop a pathology agent system to assess image quality and filter samples, resulting in TCGA-200K, a dataset containing 217,030 pathology patches selected from the public TCGA repository. Second, we train a generative model on the screened data. In addition to image synthesis, its backbone learns structural and semantic representations that can be transferred to downstream tasks. Third, we adapt the pretrained generative backbone to semantic segmentation using a lightweight task-specific head. We further introduce PathSeg-380K, a semantic segmentation dataset with expert annotations for approximately 380,000 cells across 12 cell types. Experiments on multiple pathology datasets show that the proposed framework achieves state-of-the-art segmentation performance. Our code is publicly available at https://github.com/script-Yang/Agent-GenPath.