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6 papers in the last four weeks, down 40% on the four weeks before. 0.1% of all new papers.

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People-Centred Medical Image Analysis via Fairness-Aware Human-AI Cooperation
Apr 28, 2026Zheng Zhang, Milad Masroor, Cuong Nguyen +6Human-Ai Collaboration Algorithmic Fairness
CVSSP, PAI, University of Surrey, UK · Southampton Solent University, UK · AIML, Adelaide University, Australia +2
Hierarchical Spatio-Channel Clustering for Efficient Model Compression in Medical Image Analysis
Apr 25, 2026Sisipho Hamlomo, Marcellin Atemkeng, Habte Tadesse Likassa +5Compressed Model Low-Rank Structure
Department of Mathematics, Rhodes University, Makhanda 6140, South Africa · Department of Statistics, Rhodes University, Makhanda 6140, South Africa · National Institute for Theoretical and Computational Sciences, Stellenbosch 7600, South Africa +8
Attend what matters: Leveraging vision foundational models for breast cancer classification using mammograms
Apr 21, 2026Samyak Sanghvi, Piyush Miglani, Sarvesh Shashikumar +3Mammography Cancer Detection
Department of Computer Science and Engineering, IIT Delhi, New Delhi, India · Yardi School of AI, IIT Delhi, New Delhi, India · Department of Radiodiagnosis, PGIMER Chandigarh, Chandigarh, India
Medical Image Understanding Improves Survival Prediction via Visual Instruction Tuning
Apr 20, 2026Xixi Liu, Jorge Lazo, Andreas Hallqvist +8Survival Predictions Medical Images
Chalmers University of Technology, Gothenburg, Sweden
Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis
Apr 17, 2026Ayhan Can Erdur, Daniel Scholz, Jiazhen Pan +3Medical Images Image-Text Pairs
Department of Radiation Oncology, TUM University Hospital, Munich, Germany · Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany · Chair for AI for Image-Guided Diagnosis and Therapy, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany +3
Co-distilled attention guided masked image modeling with noisy teacher for self-supervised learning on medical images
Apr 16, 2026Jue Jiang, Aneesh Rangnekar, Harini VeeraraghavanSemi-Supervised Medical Image Segmentation Swin
Memorial Sloan Kettering Cancer Center
MedLayBench-V: A Large-Scale Benchmark for Expert-Lay Semantic Alignment in Medical Vision Language Models
Apr 7, 2026Han Jang, Junhyeok Lee, Heeseong Eum +1Medical Vision-Language Models Multimodal Clinical Data
Seoul National University · Department of Radiology, Seoul National University Hospital · The Advanced Imaging and Computational Neuroimaging (AICON) Laboratory +2
Beyond Medical Diagnostics: How Medical Multimodal Large Language Models Think in Space
Mar 14, 2026Quoc-Huy Trinh, Xi Ding, Yang Liu +7Medical Images Multimodal Large Language Models
Differential privacy representation geometry for medical image analysis
Mar 1, 2026Soroosh Tayebi Arasteh, Marziyeh Mohammadi, Sven Nebelung +1Δ)$-Differential Privacy Medical Images
Lab for AI in Medicine, RWTH Aachen University, Aachen, Germany · Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany · INDA Institute, RWTH Aachen University, Aachen, Germany
Confidence-Uncertainty Boundary Calibration for Bayesian Deep Learning in Medical Image Analysis
Feb 12, 2026Hua Xu, Julián D. Arias-Londoño, Juan I. Godino-LlorenteCalibrated Uncertainty Medical Images
Escuela Técnica Superior de Ingenieros de Telecomunicación Universidad Politécnica de Madrid 28040, Madrid, Spain
LEAD: Layer-wise Expert-aligned Decoding for Faithful Radiology Report Generation
Feb 4, 2026Ruixiao Yang, Yuanhe Tian, Di Dong +3Radiology Report Generation Medical Vision-Language Models
Beijing Institute of Technology · Zhongguancun Academy · Zhongguancun Institute of Artificial Intelligence +1
M3CoTBench: Benchmark Chain-of-Thought of MLLMs in Medical Image Understanding
Jan 13, 2026Juntao Jiang, Jiangning Zhang, Yali Bi +7Medical Images Diagnostic Benchmark
1Zhejiang University · University of Science and Technology of China · 3East China Normal University +2
A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding
Dec 24, 2025Christina Liu, Alan Q. Wang, Joy Hsu +2Medical Images Multimodal Clinical Data
California Institute of Technology, Pasadena, CA 91125 · Stanford University, Stanford, CA 94305
Generalised Medical Phrase Grounding
Nov 30, 2025Wenjun Zhang, Shekhar S. Chandra, Aaron NicolsonRadiology Report Generation Medical Images
The University of Queensland, Brisbane, Australia · Australian e-Health Research Centre, CSIRO Health and Biosecurity, Australia
MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images
Nov 15, 2025Qinyue Tong, Ziqian Lu, Jun Liu +3Semi-Supervised Medical Image Segmentation Medical Images
Zhejiang University, Hangzhou, Zhejiang, China · Zhejiang Sci-Tech University, Hangzhou, Zhejiang, China · National University of Singapore, Singapore
Early Intervention for VFM-based Multimodal Medical Image Classification
Date pendingQijie Wei, Hailan Lin, Xirong LiMedical Image Classification Medical Images
Renmin University of China, Beijing 100872, China
Solve the Missing First Step: Can VLMs Standardize Raw Heterogeneous Medical Data?
Date pendingXin Chen, Dongliang Xu, Cunhao Zhu +5Medical Vision-Language Models Diagnostic Benchmark
Shandong University · *Corresponding authors. · Stanford University
From Few-Shot Segmentation to Clinician-in-the-Loop Medical Image Analysis
Date pendingYazhou ZhuSemi-Supervised Medical Image Segmentation Medical Images

Apr 28, 2026, cs.LGJ/K move · Enter open · S save

People-Centred Medical Image Analysis via Fairness-Aware Human-AI Cooperation

Zheng Zhang, Milad Masroor, Cuong Nguyen, Tahir Hassan+5

CVSSP, PAI, University of Surrey, UK · Southampton Solent University, UK · AIML, Adelaide University, Australia+2

Human-Ai Collaboration Algorithmic Fairness Medical Imaging Datasets Medical Images Subgroups Roc-Auc

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Machine learning models for medical image analysis often exhibit subgroup-dependent performance, which impacts how decisions should be allocated between automated systems and human experts under limited resources. Prior work on AI fairness and human-AI cooperation, including learning to defer (L2D) and learning to complement (L2C), typically addresses these problems in isolation. We propose People-Centred Medical Image Analysis (PecMan), a framework for fairness-aware human-AI co-operative classification that jointly models subgroup-dependent reliability, decision allocation, and collaborative prediction. PecMan combines subgroup-specialised predictors with a gating and consolidation mechanism that dynamically assigns cases to automated models, human experts, or their combination, without requiring sensitive attributes at test time. We also introduce the FairHAI benchmark for evaluating trade-offs between predictive accuracy, subgroup equity, and human involvement. In addition, we provide a theoretical analysis of multi-agent gating via selection regret and characterise fairness-coverage trade-offs under input-dependent allocation. Experiments across multiple medical imaging datasets demonstrate that PecMan achieves consistently improved trade-offs compared to methods that address fairness or human-AI cooperation separately.

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