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.

A multifractal-based masked auto-encoder: an application to medical images
May 25, 2026Joao Batista Florindo, Viviane de MouraMasked Autoencoders Medical Image Classification
Institute of Mathematics, Statistics and Scientific Computing - University of Campinas, Rua Sérgio Buarque de Holanda, 651, Cidade Universitária ”Zeferino Vaz” - Distr. Barão Geraldo, CEP 13083-859, Campinas, SP, Brasil
Benchmarking Convolutional, Transformer, Hybrid, and Vision Language Models for Multi Disease Retinal Screening
May 25, 2026Durjoy Dey, Aymane Ajbar, Yuhong YanHybrid CNN-Transformer Architectures Vision Transformer
Department of Computer Science and Software Engineering Concordia University Montreal, QC, Canada · Ebovir Biotechnologie Inc. Montreal, QC, Canada
Which Anatomy Matters Under Limited Labels? A Data-Efficient Anatomy-Aware Benchmark for Cardiac Pathology Prediction
May 25, 2026Himanshu SinghHealthcare Medical Image Analysis
Computing & Artificial Intelligence Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
ARMA-C3: A Contrastive ARMA Convolutional Framework for Unsupervised and Semi-supervised Classification
May 25, 2026VSS Tejaswi Abburi, Saurabh J. Shigwan, Nitin KumarGraph Neural Networks Unsupervised Representation Learning
Department of Computer Science and Engineering, Shiv Nadar Institute of Eminence Deemed to be University, Delhi NCR, India
How Far Has AI Come in Liver Fibrosis Staging? A Large-Scale Real-World Dataset and Benchmark
May 25, 2026Yuanye Liu, Nannan Shi, Zhejia Zhang +20Medical Image Classification Medical Image Benchmarks
School of Data Science, Fudan University, Shanghai, China · Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China · Department of Electrical and Computer Engineering, Northwestern University, Evanston, USA +12
Cross-Stage Attention Multi-Expert Network for Radiologist-Inspired Breast Ultrasound Diagnosis
May 25, 2026Xinyang Zhai, Chong Yang, Ruizhi ZhangMixture of Experts Medical Image Classification
X-Edit: Exact, Explicit, and Explainable Null-Space Editing for Medical Vision Transformers
May 24, 2026Yuanye Liu, Siyuan Zhou, Ke Zhang +3Vision Transformer Medical Image Classification
Fudan University, Shanghai, China · Johns Hopkins University, Baltimore, USA · National University of Singapore, Singapore +1
Explainable Multi-Task Retinal Imaging Reveals Microvascular Signals for Systemic Risk Stratification in Type 2 Diabetes: A Pilot Study
May 24, 2026Mini Han Wang, Liting Huang, Wei Hong +1Medical Image Analysis Medical Image Classification
Faculty of Computer Science and Artificial Intelligence, Shenzhen University of Advanced Technology, Shenzhen, P.R.China · Frontier Science Computing Center, Zhuhai Institute of Advanced Technology Chinese Academy of Sciences, Zhuhai, P.R.China · Chinese University of Hong Kong, Hong Kong China +2
Self-Supervised Contrastive Learning for Cardiac MR Sequence Classification
May 24, 2026Yuli Wang, Hyewon Jung, Dongshen Peng +10Vision Transformer Vision Foundation Model Adaptation
Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA · Department of Electrical and Computer Engineering, Johns Hopkins University,2026 Baltimore, MD, 21218, USA · Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 +4
From Theory to Decision Rule: Calibrating the Noisy-Label Crossover for Vision-Language Model Weak Supervision Across Three Medical-Imaging Benchmarks
May 23, 2026Bruce Changlong Xu, Jose James, Alexander RyuVision-Language Models Medical Image Classification
Department of Computer Science, Stanford University, Stanford, CA, USA · Mayo Clinic, Rochester, MN, USA.
Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification
May 23, 2026Nikola Cenikj, Özgün Turgut, Alexander Müller +5Medical Imaging Coronary Angiography
Chair for AI in Healthcare and Medicine, Technical University of Munich and TUM University Hospital, Munich, Germany · Department of Internal Medicine, TUM University Hospital, Munich, Germany · Department of Computing, Imperial College London, UK
MedicalRec: Medical recommender system for image classification without retraining
May 23, 2026Roghayeh Taghavi, Aysa Hasanazde Bashkandi, Amir Ali Bengari +5Model Selection Healthcare
MuellerPT: Decomposition Driven Pretraining for Dense Learning in Mueller Polarimetry
May 22, 2026Adam Tlemsani, Yingdian Li, Maxime Giot +4Self-Supervised Pre-Training Medical Image Classification
Department of Computing, Imperial College London · Hamlyn Centre for Robotic Surgery, Imperial College London · Department of Surgery and Cancer, Imperial College London +2
Enhancing Blood Cells Classification using Hybrid Quantum Neural Networks
May 22, 2026Guilherme Cruz, Nouhaila Innan, Alberto Marchisio +2Hybrid Quantum-Classical ML Medical Image Classification
Instituto de Telecomunicac¸˜oes, University of Coimbra, Portugal · eBrain Lab, Division of Engineering, New York University Abu Dhabi, PO Box 129188, Abu Dhabi, UAE · Center for Quantum and Topological Systems, NYUAD Research Institute, New York University Abu Dhabi, UAE +1
Do Synthetic Brain MRIs Reliably Improve Tumour Classification? A StyleGAN2-ADA Class-Plane Augmentation Study on BRISC 2025
May 21, 2026José Rafael Noriega CedeñoSynthetic Data Augmentation Medical Image Classification
Faculty of Applied Sciences and Technology
Case-Aware Medical Image Classification with Multimodal Knowledge Graphs and Reliability-Guided Refinement
May 21, 2026Yiming Xu, Yixuan Liu, Yuhang Zhang +3Medical Image Classification Multimodal Graph Learning
University of Science and Technology of China · Hefei, China
Entropy-Guided Self-Supervised Learning for Medical Image Classification
May 21, 2026Joao Florindo, Viviane MouraSelf-Supervised Pre-Training Medical Image Classification
Institute of Mathematics, Statistics and Scientific Computing Department of Applied Mathematics, University of Campinas Rua Sergio Buarque de Holanda, 651, Campinas, 13083-859, Sao Paulo, Brazil
HADS-Net:A Hybrid Attention-Augmented Dual-Stream Network with Physics-Informed Augmentation for Breast Ultrasound Image Classification
May 19, 2026Chinedu Emmanuel Mbonu, Blessing Nwamaka Iduh, Joseph Ikechukwu Odo +1Data Augmentation Medical Image Classification
Department of Computer Science, Nazarbayev University, Astana · Department of Computer Science, Nnamdi Azikiwe University Awka, Nigeria · Department of Computer Science, Peter University Achina-Onneh, Nigeria
Understanding Model Behavior in Monocular Polyp Sizing
May 19, 2026Xinqi Xiong, Andrea Dunn Beltran, Junmyeong Choi +3Endoscopy Medical Image Classification
University of North Carolina at Chapel Hill · University of California, San Diego
A Comprehensive Comparison of Deep Learning Architectures for COVID-19 Classification on CT & X-ray Imagery
May 19, 2026Sarmad Khan, Arslan Shaukat, Umer Asgher +1Chest X-Ray Classification Medical Image Classification
Department of Computer & Software Engineering National University of Sciences & Technology Islamabad, Pakistan · School of Computing & Information Systems University of Melbourne Melbourne, Australia
Synergistic Foundation Models for Semi-Supervised Fetal Cardiac Ultrasound Analysis: SAM-Med2D Boundary Refinement and DINOv3 Semantic Enhancement
May 19, 2026Tonghao Zhuang, Shanglong Hu, Yongsheng Luo +2Ultrasound Image Segmentation Medical Imaging Foundation Models
Zhuhai College of Science and Technology, Zhuhai, China
Neuron Incidence Redistribution for Fairness in Medical Image Classification
May 19, 2026Abin Shoby, Lyle John Palmer, Nikhil Cherian KurianAlgorithmic Fairness Medical Image Classification
Australian Institute for Machine Learning · Adelaide University · Adelaide, SA 5000, Australia
MAM-CLIP: Vision-Language Pretraining on Mammography Atlases for BI-RADS Classification
May 19, 2026Halil Ibrahim Gulluk, Olivier GevaertMedical Imaging Medical Image Classification
Department of Electrical Engineering Stanford University Stanford, CA 94305 · Biomedical Informatics Research (BMIR) Stanford University Stanford, CA 94304
Robust Mitigation of Age-Dependent Confounding Effects via Sample-Difficulty Decorrelation
May 19, 2026Nikhil Cherian Kurian, Victor Caquilpan Parra, Abin Shoby +2Distribution Shift Robustness Algorithmic Bias
Australian Institute for Machine Learning, Adelaide University, Adelaide, Australia
Worst-Group Equalized Odds Regularization for Multi-Attribute Fair Medical Image Classification
May 19, 2026Nikhil Cherian Kurian, Victor Caquilpan Parra, Abin Shoby +6Algorithmic Fairness Medical Image Classification
Australian Institute for Machine Learning, Adelaide University, Adelaide, Australia · GlaxoSmithKline (GSK)
Quantized Machine Learning Models for Medical Imaging in Low-Resource Healthcare Settings
May 19, 2026Sumanth Meenan Kanneti, Aryan ShahEfficient Neural Network Inference Medical Image Classification
Georgia State University · University of Southern California
On-Device Continual Learning with Dual-Stage Buffer and Dynamic Loss for Point-of-Care Pneumonia Diagnosis
May 19, 2026Danu KimMedical Diagnosis Continual Learning
Korea International School, Jeju Campus, Jeju-do 63644, Republic of Korea
A World Model of Radiologist Reading for Medical Image Representation Learning
May 17, 2026Yiwei Li, Zihao Wu, Huaqin Zhao +5World Model Learning Healthcare
University of Georgia · University of Texas at Arlington · New Jersey Institute of Technology
Systematic Evaluation of Vision Transformers for Automated Cervical Cancer Classification: Optimization, Statistical Validation, and Clinical Interpretability
May 17, 2026Nisreen Albzour, Sarah S. LamVision Transformer Efficient ViTs
School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA
The Learnability Gap in Medical Latent Diffusion
May 16, 2026Mischa Dombrowski, Felix Nützel, Bernhard KainzLatent Diffusion Models Medical Image Classification
FAU Erlangen-Nürnberg, Erlangen, DE · Department of Computing, Imperial College London, London, UK

May 25, 2026J/K move · Enter open · S save

A multifractal-based masked auto-encoder: an application to medical images

Joao Batista Florindo, Viviane de Moura

Institute of Mathematics, Statistics and Scientific Computing - University of Campinas, Rua Sérgio Buarque de Holanda, 651, Cidade Universitária ”Zeferino Vaz” - Distr. Barão Geraldo, CEP 13083-859, Campinas, SP, Brasil

Masked Autoencoders Medical Image Classification Medical Image Representation Learning Masked Image Modeling

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Masked autoencoders (MAE) have shown great promise in medical image classification. However, the random masking strategy employed by traditional MAEs may overlook critical areas in medical images, where even subtle changes can indicate disease. To address this limitation, we propose a novel approach that utilizes a multifractal measure (Renyi entropy) to optimize the masking strategy. Our method, termed Multifractal-Optimized Masked Autoencoder (MO-MAE), employs a multifractal analysis to identify regions of high complexity and information content. By focusing the masking process on these areas, MO-MAE ensures that the model learns to reconstruct the most diagnostically relevant features. This approach is particularly beneficial for medical imaging, where fine-grained inspection of tissue structures is crucial for accurate diagnosis. We evaluate MO-MAE on several medical datasets covering various diseases, including MedMNIST and COVID-CT. Our results demonstrate that MO-MAE achieves promising performance, surpassing other basiline and state-of-the-art models. The proposed method also adds minimum computational overhead as the computation of the proposed measure is straightforward. Our findings suggest that the multifractal-optimized masking strategy enhances the model's ability to capture and reconstruct complex tissue structures, leading to more accurate and efficient medical image representation. The proposed MO-MAE framework offers a promising direction for improving the accuracy and efficiency of deep learning models in medical image analysis, potentially advancing the field of computer-aided diagnosis.