医学 AI

Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology

Alex Foote, Amina Asif, Ayesha Azam, Tim Marshall-Cox, Nasir Rajpoot, Fayyaz Minhas

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.LG、eess.IV

Comments 10 pages

PS-DeVCEM: Pathology-sensitive deep learning model for video capsule endoscopy based on weakly labeled data

A. Mohammed, I. Farup, M. Pedersen, S. Yildirim, Ø Hovde

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.CV、cs.LG

Journal ref Computer Vision and Image Understanding 201 (2020): 103062

Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations

Navid Alemi Koohbanani, Balagopal Unnikrishnan, Syed Ali Khurram, Pavitra Krishnaswamy, Nasir Rajpoot

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.CV、eess.IV

Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification

Koen Dercksen, Wouter Bulten, Geert Litjens

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.CV、eess.IV

Comments 4 pages, 3 figures,MIDL 2019 [arXiv:1907.08612] extended abstract

Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization

Tian Xia, Agisilaos Chartsias, Sotirios A. Tsaftaris

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.CV、cs.LG

Focus Quality Assessment of High-Throughput Whole Slide Imaging in Digital Pathology

Mahdi S. Hosseini, Yueyang Zhang, Lyndon Chan, Konstantinos N. Plataniotis, Jasper A. Z. Brawley-Hayes, Savvas Damaskinos

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.CV、eess.IV

Comments 10 pages, This work has been submitted to the IEEE for possible publication

Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis

Baris Gecer, Ozge Yalcinkaya, Onur Tasar, Selim Aksoy

专题命中 病理影像 ：diagnosis(title,abstract)；分类 cs.CV、cs.LG

SAGE: Semantic Explainability of Attention-Based Survival Models in Computational Pathology

SAGE：计算病理学中基于注意力的生存模型的语义可解释性

Abdallah Lamane, Abdul Rahman Diab, Ren-Chin Wu, William Lotter

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.CV；medical image(comments)

AI总结 该研究提出SAGE框架，可从ABMIL模型提取全局语义解释，在7个TCGA癌症队列的生存预测中恢复预后特征，揭示癌症特异性生物学，为病理学家解释模型行为提供支持。

Comments Proceedings of the MICCAI Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC)

Prompt, Plan, Extract: Zero-Shot Agentic LLMs Workflows for Lung Pathology Extraction from Clinical Narratives

提示、规划、提取：用于从临床叙述中提取肺部病理学的零样本智能体LLM工作流

Aman Pathak, Cheng Peng, Mengxian Lyu, Ziyi Chen, Reema Solan, Sankalp Talankar, Yasir Khan, Hiren Mehta, Aokun Chen, Yi Guo, Yonghui Wu

机构 * Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida（健康结果与生物医学信息学系，医学院，佛罗里达大学） ； Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, College of Medicine, University of Florida（呼吸科、重症医学科和睡眠医学科，医学系，医学院，佛罗里达大学） ； College of Nursing, Florida State University（护理学院，佛罗里达州立大学） 纠错

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.LG；biomedical(comments)

AI总结 提出零样本智能体工作流，利用开源大语言模型从肺切除病理报告中提取13个CAP字段，在无训练下达到0.893 Micro-F1，接近监督方法。

Comments 7 pages, 2 figures, 3 tables. Affiliations: (1) Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA (2) Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA (3) College of Nursing, Florida State University, Tallahassee, FL, USA

Explaining Digital Pathology Models via Clustering Activations

通过激活聚类解释数字病理学模型

Adam Bajger, Jan Obdržálek, Vojtěch Kůr, Rudolf Nenutil, Petr Holub, Vít Musil, Tomáš Brázdil

机构 * 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） 纠错

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.CV；biomedical(journal_ref)

AI总结 提出一种基于卷积神经网络激活聚类的可解释性方法，通过展示模型全局行为并提供细粒度信息，增强对数字病理学模型的理解和信任。

Journal ref 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)

Reproducible Machine Learning-based Voice Pathology Detection: Introducing the Pitch Difference Feature

Jan Vrba, Jakub Steinbach, Tomáš Jirsa, Laura Verde, Roberta De Fazio, Yuwen Zeng, Kei Ichiji, Lukáš Hájek, Zuzana Sedláková, Zuzana Urbániová, Martin Chovanec, Jan Mareš, Noriyasu Homma

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.LG

Comments Code repository: https://github.com/aailab-uct/Automated-Robust-and-Reproducible-Voice-Pathology-Detection, Supplementary materials: https://doi.org/10.5281/zenodo.14793017

Joining Forces for Pathology Diagnostics with AI Assistance: The EMPAIA Initiative

Norman Zerbe, Lars Ole Schwen, Christian Geißler, Katja Wiesemann, Tom Bisson, Peter Boor, Rita Carvalho, Michael Franz, Christoph Jansen, Tim-Rasmus Kiehl, Björn Lindequist, Nora Charlotte Pohlan, Sarah Schmell, Klaus Strohmenger, Falk Zakrzewski, Markus Plass, Michael Takla, Tobias Küster, André Homeyer, Peter Hufnagl

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.CV

Journal ref Journal of Pathology Informatics 2024

Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review

Ricardo Gonzalez, Peyman Nejat, Ashirbani Saha, Clinton J. V. Campbell, Andrew P. Norgan, Cynthia Lokker

专题命中 病理影像 ：diagnosis(title,abstract)；分类 cs.CV；pathology(journal_ref)

Journal ref Journal of Pathology Informatics. 2023;15:100348

Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

David Tellez, Geert Litjens, Peter Bandi, Wouter Bulten, John-Melle Bokhorst, Francesco Ciompi, Jeroen van der Laak

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.CV；medical image(comments)

Comments Accepted in the Medical Image Analysis journal

Learning with less data via Weakly Labeled Patch Classification in Digital Pathology

Eu Wern Teh, Graham W. Taylor

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.CV；biomedical(comments)

Comments To appear in IEEE International Symposium on Biomedical Imaging (ISBI) 2020

Unsupervised Pathology Image Segmentation Using Representation Learning with Spherical K-means

Takayasu Moriya, Holger R. Roth, Shota Nakamura, Hirohisa Oda, Kai Nagara, Masahiro Oda, Kensaku Mori

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.CV

Comments This paper was presented at SPIE Medical Imaging 2018, Houston, TX, USA

Journal ref Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 1058111 (6 March 2018)

Spatial Message Passing in Language Space for Pathology Image Interpretation

面向病理图像解读的语言空间空间消息传递

Jing-Cheng Yang, Hao-Jung Wang, Jinhao Du, Yang Hu, Ming-shan Tsai, Jens Rittscher, Bin Li

机构 * National Taiwan University（台湾大学） ； University of Oxford（牛津大学） ； ZYTCA Limited（ZYTCA有限公司） 纠错

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.CV、q-bio

AI总结 提出SLMP框架，在语言空间对病理图像执行空间推理，无需微调MLLM，提升肿瘤描述准确率，缩小通用与病理专用MLLM的性能差距。

Comments Accepted at MICCAI 2026 Workshop (Oral)

What Carries the Signal in Pathology Foundation-Model Atlases? A Patient-Level Controlled Benchmark in Breast Cancer

病理基础模型图谱中的信号由什么承载？乳腺癌的患者级对照基准测试

Chimdi Walter Ndubuisi

机构 * University of Missouri（密苏里大学） 纠错

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.CV、q-bio

AI总结 本研究以乳腺癌患者为单位开展对照基准测试，发现病理基础模型的信号由嵌入而非几何机制承载，嵌入在多数基因程序预测中优于组织组成，驱动基因计数指标无显著预测性。

Comments 40 pages, 7 figures, 10 tables. Supplementary Information (16 pages) included as an ancillary file. Segmentation outputs obtained under the Aignostics Research Access Programme; OpenTME data at https://huggingface.co/datasets/Aignostics/OpenTME

Stuck on Suggestions: Automation Bias, the Anchoring Effect, and the Factors That Shape Them in Computational Pathology

陷入建议：自动化偏见、锚定效应及影响它们的因素在计算病理学中的研究

Emely Rosbach, Jonas Ammeling, Jonathan Ganz, Christof Albert Bertram, Thomas Conrad, Andreas Riener, Marc Aubreville

专题命中 病理影像 ：pathology(title,abstract)；biomedical(comments,journal_ref)

AI总结 研究探讨了AI在计算病理学中的应用，发现虽然AI提升了诊断性能，但引入了自动化偏见和锚定效应，时间压力和用户特征影响这些偏见的严重程度。

Comments Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2026:007

Journal ref Machine.Learning.for.Biomedical.Imaging. 2026 (2026)

Semi-Supervised Domain Adaptation with Latent Diffusion for Pathology Image Classification

半监督域适应与潜在扩散用于病理图像分类

Tengyue Zhang, Ruiwen Ding, Luoting Zhuang, Yuxiao Wu, Erika F. Rodriguez, William Hsu

机构 * Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles（医学与影像信息学系，放射科学系，大卫·盖弗医学院，加州大学洛杉矶分校） ； Bioengineering Department, Henry Samueli School of Engineering, UCLA（生物工程系，亨利·萨缪尔西工程学院，UCLA） ； Department of Pathology & Laboratory Sciences, David Geffen School of Medicine, UCLA（病理学与实验室科学系，大卫·盖弗医学院，UCLA） 纠错

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.CV、q-bio

AI总结 本研究提出利用潜在扩散模型生成目标领域意识的合成数据，提升计算病理学中的域泛化能力。

EXAONE Path 2.5: Pathology Foundation Model with Multi-Omics Alignment

EXAONE Path 2.5：多组学对齐的病理基础模型

Juseung Yun, Sunwoo Yu, Sumin Ha, Jonghyun Kim, Janghyeon Lee, Jongseong Jang, Soonyoung Lee

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.LG、q-bio

AI总结 EXAONE Path 2.5通过多组学对齐构建病理基础模型，实现更全面的肿瘤生物学建模，展现高效率和适应性，推动精准肿瘤学发展。

SlideMamba: Entropy-Based Adaptive Fusion of GNN and Mamba for Enhanced Representation Learning in Digital Pathology

Shakib Khan, Fariba Dambandkhameneh, Nazim Shaikh, Yao Nie, Raghavan Venugopal, Xiao Li

机构 * Computational Science and Informatics（计算科学与信息学） ； Roche Diagnostic Solutions Pathology Lab（罗氏诊断解决方案病理实验室） ； Pathology Lab, Canada（病理实验室，加拿大） ； Pathology Lab, USA（病理实验室，美国） 纠错

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.CV、q-bio

Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data

Ekaterina Redekop, Mara Pleasure, Vedrana Ivezic, Zichen Wang, Kimberly Flores, Anthony Sisk, William Speier, Corey Arnold

专题命中 病理影像 ：pathology(title,abstract)；分类 q-bio、eess.IV

BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology

Amaya Gallagher-Syed, Henry Senior, Omnia Alwazzan, Elena Pontarini, Michele Bombardieri, Costantino Pitzalis, Myles J. Lewis, Michael R. Barnes, Luca Rossi, Gregory Slabaugh

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.CV、q-bio

Comments Accepted for publication at CVPR 2025

Towards Unified Molecule-Enhanced Pathology Image Representation Learning via Integrating Spatial Transcriptomics

Minghao Han, Dingkang Yang, Jiabei Cheng, Xukun Zhang, Linhao Qu, Zizhi Chen, Lihua Zhang

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.CV、q-bio

Comments 21 pages, 11 figures, 7 tables

Cerebral microbleeds: Association with cognitive decline and pathology build-up

Saima Rathore, Jatin Chaudhary, Boning Tong, Selen Bozkurt

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.LG、q-bio

Comments 11 pages, 2 figures

Computational Pathology for Accurate Prediction of Breast Cancer Recurrence: Development and Validation of a Deep Learning-based Tool

Ziyu Su, Yongxin Guo, Robert Wesolowski, Gary Tozbikian, Nathaniel S. O'Connell, M. Khalid Khan Niazi, Metin N. Gurcan

专题命中 病理影像 ：pathology(title,abstract)；分类 q-bio、eess.IV

Impact of Stain Variation and Color Normalization for Prognostic Predictions in Pathology

Siyu, Lin, Haowen Zhou, Richard J. Cote, Mark Watson, Ramaswamy Govindan, Changhuei Yang

专题命中 病理影像 ：pathology(title,abstract)；分类 q-bio、eess.IV

Open and reusable deep learning for pathology with WSInfer and QuPath

Jakub R. Kaczmarzyk, Alan O'Callaghan, Fiona Inglis, Tahsin Kurc, Rajarsi Gupta, Erich Bremer, Peter Bankhead, Joel H. Saltz

专题命中 病理影像 ：pathology(title,abstract)；分类 cs.CV、q-bio

Improved Breast Cancer Diagnosis through Transfer Learning on Hematoxylin and Eosin Stained Histology Images

Fahad Ahmed, Reem Abdel-Salam, Leon Hamnett, Mary Adewunmi, Temitope Ayano

专题命中 病理影像 ：diagnosis(title,abstract)；分类 cs.CV、q-bio

Comments 12 pages, 4 figures, 6 tables