Medical Image Classification

Latest papers 486

53 papers in the last four weeks, up 8% on the four weeks before. 0.3% of all new papers.

Benchmarking Self-Supervised 3D Medical Vision Foundation Models and Transferability Estimation
Oct 13, 2026Jiaqi Tang, Yu Wang, Yang Liu +7Medical Imaging Foundation Models Medical Image Benchmarks
National Institution of Health Data Science, Peking University, Beijing, China. · Institute of Medical Technology, Peking University, Beijing, China. · State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, China. +5
HybridDualPathNet: Cross-Fusion Attention of EfficientNetV2 and Swin Transformer for Brain MRI Classification of Alzheimer's and Parkinson's Disease
Oct 12, 2026Jarpula Nirjala, Rahul PatelMedical Image Classification Alzheimer's Disease Classification
Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Surat, India
A Concept-informed Interpretable Artificial Intelligence Framework for Breast Ultrasound Diagnosis
Oct 12, 2026Shengzhou Zhong, Ziying Wang, Liang Li +14Clinical Decision Support Explainable Medical Image Analysis
School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China. · Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China. · Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China. +5
Generative Quantum Machine Learning Methods for Cancer Detection using Augmented Datasets
Oct 10, 2026Sophia Knutson, Nandika Goyal, Gennaro De Luca +3Medical Image Classification Synthetic Data Augmentation
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA · SenSIP Center, Arizona State University, Tempe, AZ, USA · School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
Spatiotemporal Recognition of Abdominal Wall Layers During Optical Trocar Entry
Oct 10, 2026Shreeram Athreya, Andrew Melehy, Carter Prentice +4Medical Image Analysis Surgical Video Understanding
Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, California, USA. · Department of Surgery, University of California, Los Angeles, Los Angeles, California, USA. · Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, California, USA. +2
CAPTOR: a vision-language foundation model for Chest-Abdomen-Pelvis Oncologic CT
Oct 9, 2026Séléna Jean-Mactoux, Sophie Loizillon, Ghina Jardali +3Radiology VLMs Medical Image Classification
Biomaps, UMR1281, INSERM, CNRS, CEA, Université Paris-Saclay, Villejuif, France · Guerbet Research, Villepinte, France · Department of Diagnostic Oncologic Imaging, Gustave Roussy Institute, Villejuif, France +2
Beyond Discrimination: Evaluating Probability Calibration in Radiomics-Image Fusion for Renal Cell Carcinoma Classification
Oct 9, 2026Yuan Liang, Morgan May, Abraham Campbell +3Medical Image Classification Probability Calibration
Research Ireland Centre for Research Training in Machine Learning, ML-Labs, Dublin, Ireland · School of Computer Science, University College Dublin, Dublin, Ireland · School of Computer Science, Technological University Dublin, Dublin, Ireland +2
Can Multimodal Decision Models Classify Breast Ultrasound Images? Benchmarking CLEF and CLEF-Flash Across Prompts and Datasets
Oct 9, 2026Tran Cao Minh, Hai Tran, Ha Minh TanMedical Image Classification Multimodal Classification
Research and Development Center, VNSO Technology JSC, Ho Chi Minh City, Vietnam · Department of Information Technology, Ho Chi Minh University of Education, Ho Chi Minh City, Vietnam · Faculty of Information Science and Engineering, University of Information Technology, Ho Chi Minh City, Vietnam +1
Radiomics-Based Thyroid Nodule Classification with Label-Conditional Conformal Prediction
Oct 9, 2026Laura Testa, Pierpaolo Brutti, Umberto Ferraro PetrilloMedical Image Classification Radiomics
Department of Statistical Sciences, Sapienza University of Rome, Italy.
The Predictability Bias of Joint-Embedding Predictive Learning in Medical Imaging
Oct 9, 2026Fabian Drexel, Bernhard Kainz, Jakob N. Kather +4Medical Image Representation Learning Joint-Embedding Predictive Architecture
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany · Technical University of Munich, School of Medicine and Health, TUM University Hospital, Klinikum rechts der Isar, Department of Diagnostic and Interventional Radiology, Munich, Germany · Dept. AIBE, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany +7
Learning Features, Not Layouts: Feature-Axis Attention as a Transfer Prior for Medical Imaging
Oct 9, 2026Aon Safdar, Mohamed SaadeldinMedical Image Representation Learning Vision Transformer
University College Dublin, Ireland
Healthy Counterfactual Generation via Diffusion Inpainting for Mammography Classification
Oct 8, 2026Inês Cruchinho Garcia, Mariana Mourão, Francisco Maria Calisto +2Counterfactual Data Augmentation Mammography
Institute for Systems and Robotics, Instituto Superior Técnico, Lisbon, Portugal
Contrast Enhancement or Noise Reduction? On Improving Cervical Cancer Classification
Oct 8, 2026Ach Khozaimi, Ulfatun NahdhiyahMedical Image Classification Image Enhancement
Department of Computer Science, University of Trunodjoyo Madura, Indonesia · Anatomical Pathology Laboratory, District General Hospital (RSUD Syarifah Ambami Rato Ebu), Indonesia
FedSSMCoOp: SSM Encoders for light-weight Federated Prompt Learning for Few-shot Classification
Oct 7, 2026Ankita Das, Ambarish Parthasarathy, Sumohana S. Channappayya +1Few-Shot Image Classification Multimodal Federated Learning
Department of Computer Science and Engineering Indian Institute of Technology Hyderabad · Department of Artificial Intelligence Indian Institute of Technology Hyderabad · Department of Electrical Engineering Indian Institute of Technology Hyderabad
Beyond Explanation: Debugging Medical Imaging Models via Concept Intervention
Oct 6, 2026Samrajya Thapa, Daniel J. Quest, Timothy L. Kline +3Explainable Medical Image Analysis Concept Bottleneck Models
Iowa State University, Ames IA, USA · Mayo Clinic, Rochester MN, USA
MedCORE: Criteria-Grounded Clinical Reasoning for Interpretable Medical Image Diagnosis
Oct 6, 2026Asim Khan, Samee Ullah Khan, Dwarikanath MahapatraMedical Image Classification Medical VLMs
Deparment of Computer Science, Khalifa University, Abu Dhabi, UAE · Department of Aerosapce Engineering, Khalifa University, Abu Dhabi, UAE
Knee3DVLM: Dual-Sequence Full-Volume Vision-Language Modeling for Comprehensive Knee MRI Assessment
Oct 6, 2026Maryam Baizhigitova, Andrew Seohwan Yu, Po-Hao Chen +7Medical Imaging Medical Image Analysis
Cleveland Clinic, Cleveland, OH 44106, USA · Cleveland State University, Cleveland, OH 44115, USA · Case Western Reserve University, Cleveland, OH 44106, USA +1
Hybrid Cross-Modal Attention Network for Early Breast Cancer Detection in Low-Resource Clinical Settings
Oct 5, 2026Simon Hadush Nrea, Filimon Gidey Gebremichael, Gebrekirstos Hagos Gebrekirstos +1Breast Cancer Medical Image Classification
Mekelle University, Mekelle, Ethiopia · Clinical Oncologist London School of Hygiene and Tropical Medicine London, UK
Impact of Data Augmentation on Confidence Calibration in Melanoma Classification
Oct 5, 2026Morgan May, Simon Caton, Pierpaolo DondioData Augmentation Medical Image Classification
Technological University Dublin, Dublin, Ireland · Research Ireland Centre for Research Training in Machine Learning, ML-Labs, Dublin, Ireland · University College Dublin, Dublin, Ireland +1
How well do routinely collected demographic and clinical variables aid point-of-care lung ultrasound TB classification
Oct 5, 2026Joshua M. Jansen van Vüren, Christiaan M. Geldenhuys, Devendra S. Parihar +7Medical Diagnosis Medical Image Classification
Department of Electrical and Electronic Engineering, University of Stellenbosch, Stellenbosch, South Africa · Laboratory for intelligent Global Health and Humanitarian Response Technologies (LiGHT) Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland · National Teaching Hospital for Tuberculosis and Pulmonary Diseases (CNHU-PPC), Cotonou, Benin +1
Wrong Organ, Right Physics: Transferring Echocardiography Pretraining to Lung Ultrasound for Tuberculosis Screening
Oct 2, 2026Christiaan M. Geldenhuys, Joshua M. Jansen van Vüren, Véronique Suttels +6Transfer Learning Medical Image Classification
Department of Electrical and Electronic Engineering, University of Stellenbosch, Stellenbosch, South Africa · Laboratory for intelligent Global Health and Humanitarian Response Technologies (LiGHT) Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland · National Teaching Hospital for Tuberculosis and Pulmonary Diseases (CNHU-PPC), Cotonou, Benin +1
CalCErt: Bin-wise Certification of Confidence Calibration in Medical Image Classification
Oct 2, 2026Leo Fillioux, Stergios Christodoulidis, Stergios Christodoulidis +2Model Calibration Probability Calibration
Université Paris-Saclay, CentraleSupélec, Gustave Roussy, INSERM, CDDU, IHU PRISM, Gif-sur-Yvette · ILLS · LIVIA, ILLS, ETS Montréal
Image-Based Breast Implant Detection for Mammography Dataset Curation and Near-Real-Time Deployment: Comparing Foundation Models and Task-Specific Convolutional Models
Oct 1, 2026Vasisht Ishwar, Hari Trivedi, Young Seok Jeon +5Medical Image Classification Medical Imaging Foundation Models
Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA · University of Maryland, College Park, MD, USA
ZAGNet: Zone-Aware Graph Aggregation Network for Patient-Level Lung Ultrasound Diagnosis
Oct 1, 2026Li Chen, Shubham Patil, Rashid Al Mukaddim +3Medical Image Classification Ultrasound Imaging
Philips Ultrasound Cambridge, MA, 02141
From Image Latent Space to Fuzzy Rules: Interpretable Analysis of Gastrointestinal Foundation Model
Sep 30, 2026Michael D. Vasilakakis, Dimitris K. IakovidisMedical Imaging Foundation Models Medical Image Classification
Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
Deep Learning for Longitudinal Medical Imaging: A Scoping Review
Sep 29, 2026Francesca Mussa, Divyanshu Tak, Atlas H. Avval +5Longitudinal Medical Image Analysis Medical Image Classification
Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States · Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States · Center for Intelligent Imaging, Department of Radiology & Biomedical Imaging University of California, San Francisco (UCSF) +3
Evaluating Multi-Task Morphological Concept Learning for Pulmonary Nodule Malignancy Assessment in 3D CT
Sep 29, 2026Namitha NarayananRepresentation Learning Multi-Task Learning
Independent Researcher, Kerala, India
Med-RADIO: Reducing All Medical Domains Into One via Multi-Teacher Distillation
Sep 29, 2026Chu Zhang, Haoyu Jiang, Hongyuan Zhang +2Medical Imaging Foundation Models Medical Image Classification
Center for Artificial Intelligence and Robotics, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong, China
Multi-task learning for the automatic grading of enlarged perivascular space burden using MRI
Sep 29, 2026Jesse Phitidis, William N. Whiteley, Joanna M. Wardlaw +11Multi-Task Learning Medical Image Classification
Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK · Canon Medical Research Europe, Edinburgh, UK · UK Dementia Research Institute, Centre at The University of Edinburgh, Edinburgh, UK +5
FD-AA: A Lightweight Focal-Diffuse And Attenuation-Aware Head for Incidental Abdominal Abnormality Detection in Chest CT
Sep 28, 2026Haoyan Ding, Kritika Iyer, Halid Yerebakan +9Medical Image Anomaly Detection Medical Image Classification
Siemens Medical Solutions USA, Inc., Malvern, PA, USA · University of California, Los Angeles, Los Angeles, CA, USA

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Benchmarking Self-Supervised 3D Medical Vision Foundation Models and Transferability Estimation

Jiaqi Tang, Yu Wang, Yang Liu, Jing Tao+6

National Institution of Health Data Science, Peking University, Beijing, China. · Institute of Medical Technology, Peking University, Beijing, China. · State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, China.+5

Medical Imaging Foundation Models Medical Image Benchmarks Task Transferability Estimation Self-Supervised Pre-Training 3D Medical Image Segmentation Medical Image Classification

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Self-supervised pretraining has expanded the pool of 3D medical foundation models, but published evaluations use different datasets, endpoints and fine-tuning protocols. Models also differ in architecture, pretraining objective and data, while downstream targets range from voxel-level masks to patient-level diagnoses and outcomes. As a result, published findings provide little directly comparable evidence for choosing a model for a new task. Direct comparison of adaptation performance requires fine-tuning candidates on labelled target data, at substantial computational cost. We benchmark 23 candidates across the same 14 segmentation, classification and regression tasks spanning multiple anatomies and modalities. No candidate ranked among the top three across all segmentation tasks or across all classification and regression tasks; a convolutional encoder could outperform a transformer trained with the same objective on one task but underperform it on another. We benchmark transferability estimators against the fine-tuned rankings and find that their agreement varies across dense segmentation, classification and regression. InsightRank uses task-appropriate frozen-feature statistics and architecture-aware pairwise comparisons to rank candidates before fine-tuning. Across the 23-model pool, mean Kendall tau-b was 0.431 for segmentation and 0.352 for classification and regression. The segmentation Top 3 contained a task winner on four of nine tasks; the image-level Top 3 contained none on the five tasks, despite better full-ranking agreement than the matched conventional estimators. A three-model shortlist reduced recorded extraction-plus-training job time by 85.8% across seven segmentation tasks. Together, the benchmark provides a common reference for model selection and InsightRank offers a lower-cost way to prioritize fine-tuning, with shortlist performance that differs by target type.