Medical Image Benchmarks

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20 papers in the last four weeks, up 25% on the four weeks before. 0.2% of all new papers.

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Do Diabetic Foot Ulcer Segmentation Models Generalize? A Cross-Dataset Benchmark of CNN and Transformer Architectures
Jun 27, 2026Abderrahmane BenfatahMedical Diagnosis Medical Image Segmentation
King Saud University, Riyadh, Saudi Arabia
A Neuroimaging Simulation Framework for Developing and Evaluating Causal AI
Jun 27, 2026Eryn Libert-Scott, Emma A. M. Stanley, Vibujithan Vigneshwaran +3Image Generation Causal Discovery
Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada · Department of Radiology, the Hotchkiss Brain Institute, the Alberta Children’s Hospital Research Institute, and Department of Clinical Neuroscience, all at the University of Calgary, Calgary, Alberta, Canada · Department of Computing, Imperial College London, London, U.K. +1
IMCBench: A benchmark for multimodal LLMs in Image-grounded Medical Conversations
Jun 26, 2026Maria Xenochristou, Ashutosh Joshi, Korosh Vatanparvar +10Multimodal Large Language Models Clinical Language Model Evaluation
Amazon Health AI
Aloe-Vision: Robust Vision-Language Models for Healthcare
Jun 25, 2026Jaume Guasch-Martí, Enrique Lopez-Cuena, Martín Suárez-Fernández +3VLM Evaluation VLM Robustness
Barcelona Supercomputing Center (BSC)
CORTEX: A Structured Reasoning Benchmark for Trustworthy 3D Chest CT MLLMs
Jun 25, 2026Hashmat Shadab Malik, Anees Ur Rehman Hashmi, Numan Saeed +3Radiology Report Generation 3D Medical Imaging
MBZUAI, Abu Dhabi, UAE · Hasso Plattner Institute, Potsdam, Germany · Khalifa University, Abu Dhabi, UAE
FunPiQ: A New Benchmark for Pixel-Level Quality Assessment in Fundus Images
Jun 24, 2026Pengwei Wang, José Morano, Virginia Mares +1Fundus Imaging Explainable Medical Image Analysis
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria · Christian Doppler Lab for Artificial Intelligence in Retina, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
Text Over Image: Auditing Multimodal Robustness in Synthetic Medical Image Detection
Jun 24, 2026Ching-Hao Chiu, Hao-Wei Chung, Gelei Xu +7Multimodal Robustness Medical VLMs
University of Notre Dame, Notre Dame, IN, USA. · IBM Research, Yorktown Heights, NY, USA. · Department of Cardiology, Boston Children’s Hospital, Boston, MA, USA. +2
Multilingual Hematology Visual Question Answering Dataset
Jun 24, 2026Hajra Malik, Hafiza Tooba Aftab, Abdul Rehman +2Medical VQA Medical VLMs
Information Technology University, Lahore · King Edward Medical University, Lahore
BenchX: Benchmarking AI Models for Cancer Detection and Localization with Demographic and Protocol Biases
Jun 23, 2026Qi Chen, Wenxuan Li, Pedro R. A. S. Bassi +14Distribution Shift Medical Image Analysis
Johns Hopkins University · German Cancer Research Center · University Hospital Basel +10
A Leakage-Aware Comparative Benchmark of Machine Learning, Deep Learning, and Transformer Models for Reliable Leukemia Detection
Jun 23, 2026Nisreen AlbzourData Leakage Medical Image Classification
School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY, USA
Performance and Interpretability of Convolutional, Transformer, and Hybrid Deep Learning Models in Colorectal Histology Classification
Jun 21, 2026Reza BozorgpourHybrid CNN-Transformer Architectures Vision Transformer
College of Engineering & Applied Science, Department of Biomedical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
Large Language Model-Assisted Cleaning of Report-Derived Labels in a Large-Scale Chest CT Dataset
Jun 21, 2026Yosuke Yamagishi, Atsushi Takamatsu, Mototsugu Sato +4Training Data Curation LLM-Assisted Annotation
Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan · Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan · Department of Radiology, Kanazawa University Hospital, Ishikawa, Japan +3
SAGE: An Expert-Annotated South Asian GI Endoscopy Dataset for Multimodal Learning and Hallucination Analysis
Jun 20, 2026Niyoj Oli, Sachin Acharya, Sandesh Pokhrel +7Endoscopy Medical Imaging
Nepal Applied Mathematics and Informatics Institute for Research, Nepal · University of Utah, USA · GastroIntestinal Department, Dhulikhel Hospital, Nepal +3
Scaling up fine-grained intracranial vessel annotations in computed tomography angiography
Jun 19, 2026Chu-Hsuan Lin, Alberto Mario Ceballos-Arroyo, Jisoo Kim +4Medical Image Segmentation Computed Tomography
Brigham and Women’s Hospital, Harvard Medical School Boston, MA, USA · Khoury College of Computer Sciences, Northeastern University Boston, MA, USA · Washington University in St. Louis St. Louis, MI, USA +1
NoduLoCC2026: Lung Nodule Localization and Classification Contest from Chest X-Ray Images
Jun 19, 2026Adnan Mustafic, Halim Benhabiles, Adnane Cabani +19Chest X-Ray Classification Medical Image Anomaly Detection
Universit´e de Haute-Alsace, IRIMAS, 68100 Mulhouse, France
MEDLAYXPLAIN: Benchmarking the Expert-Lay Gap in Medical Vision-Language Models
Jun 19, 2026Han Jang, Junhyeok Lee, Songsoo Kim +4VLM Evaluation Medical VLMs
1Seoul National University · 2Seoul National University Hospital · 3Seoul National University College of Medicine +1
MammoExpert: Benchmarking Chain-of-Thought Reasoning in Mammography Diagnosis
Jun 19, 2026Di Dai, Bo Liu, Youcheng Li +9Medical Imaging CoT Reasoning
State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University Beijing, China · School of Computer Science and Engineer, Beijing University of Aeronautics and Astronautics Beijing, China · Center for Data Science, Peking University Beijing, China +5
CheXpercept: A Benchmark for Evaluating Expert-Level Lesion Perception in Chest X-rays
Jun 19, 2026Geon Choi, Hangyul Yoon, Nalee Kim +5VLM Evaluation Medical Imaging
1KAIST · 2Samsung Medical Center · 3Konkuk University Medical Center
GIM-ENDO: A Multimodal Endoscopic Image and Video Dataset for Gastric Intestinal Metaplasia Morphology and Pathology
Jun 18, 2026Mojgan Forootan, Mahziar Setayeshfar, Ali Darvishi +2Endoscopy Medical Imaging
Gastroenterology and Liver Disease Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran · Iran University of Medical Sciences, Tehran, Iran · Shiraz University of Medical Sciences, Shiraz, Iran +2
HEad and neCK TumOR (HECKTOR) 2025: Benchmark of Segmentation, Diagnosis, and Prognosis in Multimodal PET/CT
Jun 18, 2026Numan Saeed, Salma Hassan, Shahad Hardan +27Cancer Survival Prediction PET/CT Imaging
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE. · Amsterdam UMC, Amsterdam, The Netherlands. · The Netherlands Cancer Institute, Amsterdam, The Netherlands. +22
A Controlled Benchmark of Quantum-Latent GAN Augmentation for Brain MRI
Jun 17, 2026Syed Mujtaba Haider, Silvia FiginiSynthetic Data Augmentation Quantum Generative Modeling
Department of Political and Social Sciences, University of Pavia, Pavia, Italy
A Multi-Center Benchmark for Abdominal Disease Diagnosis and Report Generation from Non-Contrast CT
Jun 15, 2026Mariam Elbakry, Aliaa Sayed Sheha, Salma Hassan Tantawy +5Radiology Report Generation Medical Image Classification
Ain Shams University, Cairo, Egypt · University of Catania, Catania, Italy · Universitat de Barcelona, Barcelona, Spain +1
Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection
Jun 15, 2026Markus Bujotzek, Dimitrios Bounias, Stefan Denner +4Medical Image Segmentation Medical Image Benchmarks
Division of Medical Image Computing, Germany Cancer Research2026 Center, Heidelberg, 69120, Germany. · Medical3HeidelbergFaculty,InstituteUniversityof Radiationof Heidelberg,OncologyHeidelberg,(HIRO), National69120, Germany. · 3HeidelbergFaculty,InstituteUniversityof Radiationof Heidelberg,OncologyHeidelberg,(HIRO), National69120, Germany. +2
A Comprehensive Survey of Medical Image Segmentation: Challenges, Benchmarks, and Beyond
Jun 15, 2026Pengyu Zhu, Xiaojing Zhang, Kunbo Zhang +2U-Net Medical Image Segmentation
School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, China · SPIC Digital Technology Co., Ltd, Beijing, 102209, China · Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China +1
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
OpenMedReason: Scientific Reasoning Supervision for Medical Vision-Language Models
Jun 10, 2026Negin Baghbanzadeh, Pritam Sarkar, Michael Colacci +6Medical VQA Medical VLMs
York University · Vector Institute · University of British Columbia +5
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
A Controlled Audit of Pretraining Contamination in Public Medical Vision-Language Benchmarks
Jun 8, 2026Bruce Changlong Xu, Lan Wu, Alexander RyuVLM Evaluation Medical VQA
Stanford University Stanford, CA, USA · University of California, Berkeley Berkeley, CA, USA · Mayo Clinic Rochester, MN, USA
C3VD-DEFCOL: A Deformable Colonoscopy Dataset with Time-Resolved 3D Ground Truth and Realistic Appearance
Jun 5, 2026Ethan Luk, Mayank V. Golhar, Anthony Song +5Endoscopy 3D Reconstruction
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA · Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza, Zaragoza, Spain · Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

Jun 27, 2026, cs.CVJ/K move · Enter open · S save

Do Diabetic Foot Ulcer Segmentation Models Generalize? A Cross-Dataset Benchmark of CNN and Transformer Architectures

Abderrahmane Benfatah

King Saud University, Riyadh, Saudi Arabia

Medical Diagnosis Medical Image Segmentation Medical Image Benchmarks Cross-Dataset Generalization

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Deep learning models for diabetic foot ulcer (DFU) segmentation routinely report high accuracy, but they are almost always trained and tested on the same dataset, leaving their behaviour on data from a different clinical source largely unmeasured. We benchmark three representative segmentation architectures -- U-Net and DeepLabV3+ (convolutional) and SegFormer-B2 (Transformer) -- under an identical, leakage-screened protocol: training on the combined FUSeg/AZH wound data and evaluating, without fine-tuning, on two independent external datasets (DFUC2022 and Medetec). All models achieve strong in-domain performance (Dice 0.80--0.83) but degrade substantially across datasets. The degradation is, however, architecture-dependent: SegFormer-B2 generalizes best on both external sets (DFUC2022 Dice 0.557, Medetec Dice 0.786), outperforming both convolutional models, while the more complex DeepLabV3+ generalizes worse than the simpler U-Net. Per-image failure analysis on 2,160 images across both external test sets confirms that SegFormer-B2 produces the fewest catastrophic failures on DFUC2022 (31.1%), compared with U-Net (38.5%) and DeepLabV3+ (43.0%). The consistent ranking across two independent external sources, confirmed by Wilcoxon signed-rank tests (p < 0.001 on both datasets), indicates that architecture family, not model complexity, drives cross-hospital generalization.

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