Whole-Slide Image Analysis

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17 papers in the last four weeks, up 89% on the four weeks before. 0.2% of all new papers.

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TaxoMIL: Taxonomy-Constrained Learning for Hierarchical Whole Slide Image Analysis
Jun 30, 2026Chaeyeon Lee, Khang Nguyen Quoc, Jinsol Song +3Computational Pathology Hierarchical Classification
School of Electrical Engineering, Korea University, Seoul, Republic of Korea · Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul, Republic of Korea
A Multi Center Breast FNAC Whole-Slide Cytology Dataset for AI-Assisted Patch-Wise Classification Using C1 to C5 Reporting Categories
Jun 29, 2026Garima Jain, Abhijeet Patil, Surabhi Jain +36Computational Pathology Medical Image Benchmarks
Indian Council of Medical Research-National Institute for Research in Digital Health & Data Science, New Delhi · Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai · Department of Pathology, All India Institute of Medical Sciences, New Delhi +16
Uncertainty Estimation in Pathology Foundation Models via Deep Mutual Learning
Jun 29, 2026Gbègninougbo Aurel Davy Tchokponhoue, Sevda Öğüt, Ali Idri +2Pathology Foundation Models Deep Ensembles
EPFL, Lausanne, Switzerland · UM5, Rabat, Morocco
Data-Efficient Multimodal Alignment for Histopathology-based Molecular Prediction
Jun 29, 2026Dominik Winter, Dominik Vonficht, Loïc Le Bescond +6Computational Pathology Cross-Modal Alignment
AstraZeneca Computational Pathology and Biomarkers, Munich, Germany · AstraZeneca, Early Oncology and Translation Medicine, Cambridge, UK
Distribution-based deep multiple instance learning for tumor proportion scoring in NSCLC
Jun 25, 2026Krzysztof Pysz, Artur Bartczak, Jarosław Kwiecień +2Computational Pathology Oncology
Politechnika Wrocławska, Wydział Podstawowych Problemów Techniki, Katedra Inżynierii Biomedycznej, Poland · Specjalistyczny Szpital Chorób Płuc w Zakopanem, Zakład Patomorfologii, Poland · Narodowy Instytut Onkologii im. Marii Skłodowskiej-Curie, Oddział Kraków, Zakład Patomorfologii Nowotworów, Poland +1
Reducing Redundancy in Whole-Slide Image Patching for Scalable Indexing and Retrieval
Jun 23, 2026Jialiang Geng, Ghazal Alabtah, Saghir Alfasly +2Whole-Slide Image Analysis
KIMIA Lab, Dept. of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA
HERO: Hypothesis-Driven Evidence Retrieval from Omics for Multi-Task Breast Cancer Analysis
Jun 19, 2026Xiangyu Li, Ran SuBreast Cancer Clinical Outcome Prediction
College of Intelligence and Computing, Tianjin University, Tianjin 300072, China
Contrastive and Adaptive Multi-modal Masked Autoencoder for Spatial Transcriptomics
Jun 19, 2026Joohyeok Kim, Taejin Jeong, Jinyeong Kim +1Computational Pathology Spatial Transcriptomics
Department of Artificial Intelligence, Yonsei University, Seoul, Republic of Korea · Department of Computer Science, Yonsei University, Seoul, Republic of Korea · Yonsei University College of Medicine, Seoul, Republic of Korea
Semantic-Anchored Evidential Fusion for Domain-Robust Whole-Slide Survival Analysis
Jun 18, 2026Yucheng Xing, Ling Huang, Pei Liu +4Computational Pathology Cancer Survival Prediction
National University of Singapore, Singapore · Imperial College London, London, UK · Hunan University, Changsha, China
Predicting Immune Biomarkers with MultiModal Mixture-of-Expert Pathology Foundation Models Empowers Precision Oncology
Jun 16, 2026Tianyu Liu, Ziqing Wang, Zhaokang Liang +12Pathology Foundation Models Clinical Prediction
Program of Computational Biology and Bioinforamtics, Yale University, USA · Broad Institute of MIT and Harvard, USA · These authors contributed equally to this work. +12
Vision-Language Models as Zero-Annotation Oracles in Histopathology
Jun 15, 2026Vishal Jain, Giorgio Buzzanca, Sarah Cechnicka +6Computational Pathology Histopathology Image Segmentation
Imperial College London, London, UK · Leiden University Medical Center, Leiden, The Netherlands · KU Leuven, Leuven, Belgium +3
RaLMPH: Reliability-aware Learning for Multi-Pathologist Harmonization in Whole-Slide Image Classification
Jun 14, 2026Sungrae Hong, Jiwon Jeong, Soeun Cheon +5Computational Pathology Histopathology Image Classification
Korea Advanced Institute of Science and Technology, Daejeon, South Korea · Seegene Medical Foundation, Seoul, South Korea
How Seemingly Inconsequential Design Choices Dictate Performance of LLMs in Pathology
Jun 10, 2026Kian R. Weihrauch, Thomas A. Buckley, William Lotter +1Multimodal Large Language Models Histopathology Image Classification
Massachusetts Institute of Technology, Cambridge, MA · Harvard Medical School, Boston, MA · Dana-Farber Cancer Institute, Boston, MA
Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy
Jun 10, 2026Kai Standvoss, Miriam Hägele, Rosemarie Krupar +25Computational Pathology Pathology Foundation Models
Aignostics, Germany · Institute of Pathology, Charité – Universitätsmedizin Berlin, Germany · Berlin Institute of Health, Charité – Universitätsmedizin Berlin, Germany +9
AGE-MIL: Anchor-Guided Evidence Learning for Patient-Level Prediction
Jun 10, 2026Jiawei Niu, Jian Chen, Di Zhang +8Computational Pathology Weakly Supervised Learning
School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China · Department of Oncology, University of Cambridge, Cambridge, UK · Xiangya School of Medicine, Central South University, Changsha, Hunan, China
SheafStain: Sheaf-Theoretic Schrödinger Bridge for Spatially and Biologically Coherent Virtual Staining
Jun 10, 2026Hyeongyeol Lim, Hongjun Yoon, Eunjin Jang +3Computational Pathology Vision Foundation Models
Department of Medical Informatics, College of Medicine, Korea University · DEEPNOID Inc.
From Patches to Patients: A study of the tile-to-slide performance transferability in Digital Pathology
Jun 9, 2026Sofiène Boutaj, Leo Fillioux, Maria Vakalopoulou +2Pathology Foundation Models Medical Image Benchmarks
Université Paris-Saclay, CentraleSupélec, Gustave Roussy, INSERM, IHU PRISM, Cancer Data Science Unit, France · Université Paris-Saclay, CentraleSupélec, MICS Laboratory, France
GD-MIL: Grade-Disentangled Multiple Instance Learning for Multimodal Biochemical Recurrence Prediction in Prostate Cancer
Jun 8, 2026Dasari Naga RajuAdversarial Representation Learning Cancer Survival Prediction
Learnable Token Sparsification for Efficient Gigapixel Whole Slide Image Reasoning
Jun 7, 2026Jingzhi Chen, Landi He, Zhuo Chen +2Efficient VLM Inference Medical VLMs
Shenzhen University of Advanced Technology
A Multi-modal Agentic Co-pilot for Evidence Grounded Computational Pathology
Jun 6, 2026Zhe Xu, Zhengyu Zhang, Zhiyuan Cai +12Computational Pathology Clinical Decision Support
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China · Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, China · Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China +7
Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge
Jun 5, 2026Marc Aubreville, Jonas Ammeling, Sweta Banerjee +64Computational Pathology Domain Generalization
DaX: Learning General Pathology Representations Across Scales
Jun 5, 2026Bokai Zhao, Yiyang Zhang, Long Bai +3Pathology Foundation Models Medical Image Representation Learning
DAMO Academy, Alibaba Group · Institute of Automation, Chinese Academy of Sciences · Hupan Lab
LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification
Jun 5, 2026Yonghan Shin, Won-Ki JeongHistopathology Image Classification Knowledge Distillation
Department of Computer Science and Engineering, Korea University, Seoul, Korea
Pathway-Structured Privileged Distillation for Deployable Computational Pathology
Jun 1, 2026Yongxin Guo, Hao Lu, Onur Koyun +2Computational Pathology Oncology
1*School of Medicine, Wake Forest University, Winston-Salem, NC, USA.
Deep Learning for Generating Computational PIN-4 Immunohistochemistry Staining from Prostate Biopsy H&E Images
Jun 1, 2026Vietbao Tran, Pratik ShahComputational Pathology Conditional Image Generation
Biomedical Engineering University of California, Irvine Irvine, CA, USA · Pathology and Laboratory Medicine Biomedical Engineering Electrical Engineering and Computer Science University of California, Irvine Irvine, CA, USA
Simple Token-Efficient Vision-Language Model for Case-level Pathology Synoptic Report Generation
May 29, 2026Zhiyuan Yang, Jiahao Cheng, Vincent Quoc-Huy Trinh +1Computational Pathology Medical Report Generation
Department of Computer Science and Software Engineering (CSSE), Concordia University, Montreal, Canada · Axe Cancer, Centre de recherche du CHUM, Université de Montréal, Montreal, Canada · Institut de recherche en immunologie et cancérologie (IRIC), Université de Montréal
SlideCheck: Guiding Self-Supervised Pretraining of Pathology Foundation Models via Dataset Distributions
May 28, 2026Mingyi He, Xinyi Guo, Xitong Ling +7Self-Supervised Pre-Training Pathology Foundation Models
Beijing University of Chemical Technology · South China Normal University · Tsinghua University
A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation
May 25, 2026Zhengrui Guo, Zhengyu Zhang, Jiabo Ma +23Computational Pathology Clinical Decision Support
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China · Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, China · Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China +15
Benchmarking Pathology Foundation Models for Spatial Domain Understanding
May 25, 2026Bokai Zhao, Yiyang Zhang, Yuanchi Zhu +6Pathology Foundation Models Medical Image Benchmarks
School of Artificial Intelligence, University of Chinese Academy of Sciences. · Brainnetome Center, Institute of Automation, Chinese Academy of Sciences. · Beijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences. +2
Aligning Cellular Sheaves with Classifier Attention for Interpretable Weakly-Supervised Pathology Localization
May 24, 2026Devansh Lalwani, Swapnil Bhat, Maulik ShahExplainable Medical Image Analysis Medical Image Localization
Turocrates AI Private Limited, Mumbai, India

Jun 30, 2026, cs.CVJ/K move · Enter open · S save

TaxoMIL: Taxonomy-Constrained Learning for Hierarchical Whole Slide Image Analysis

Chaeyeon Lee, Khang Nguyen Quoc, Jinsol Song, Yosep Chong+2

School of Electrical Engineering, Korea University, Seoul, Republic of Korea · Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul, Republic of Korea

Computational Pathology Hierarchical Classification Histopathology Image Classification Multiple Instance Learning Whole-Slide Image Analysis

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Whole slide image (WSI) analysis is central to computational pathology, with multiple instance learning (MIL) emerging as the standard pipeline for slide-level diagnosis. However, conventional approaches formulate WSI diagnosis as a flat classification task over discrete labels, contradicting the inherently hierarchical, coarse-to-fine nature of clinical reasoning. Although recent hierarchical classifiers and vision-language models (VLMs) have sought to address this structural gap, they either fail to capture semantic continuity between related diagnoses or suffer from unconstrained text generation that produces taxonomic hallucinations and parent-child label violations. To address these limitations, we propose TaxoMIL, a taxonomy-constrained framework that reformulates WSI diagnosis as a multi-granularity text generation task. TaxoMIL utilizes a dual-head Transformer decoder to generate coarse- and fine-level diagnostic text, and introduces taxonomy-guided objectives that explicitly structure the label embedding space and strictly ground slide-level visual representations within the clinical taxonomy. Extensive experiments across three diverse WSI datasets demonstrate that TaxoMIL consistently outperforms state-of-the-art MIL classifiers and VLM-based generative methods, yielding accurate and hierarchy-aware diagnostic predictions. The code is released at https://github.com/QuIIL/TaxoMIL

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