Medical Image Classification

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Overview

Medical image classification studies how to assign diagnoses or severity grades from images such as X-rays, MRI, CT, ultrasound, fundus photographs, and pathology slides. Common approaches include convolutional neural networks, vision transformers, 2D and 3D models, and foundation models adapted through transfer learning or self-supervised pretraining; some combine images with clinical records, text, or multiple views. Work also explores interpretability, uncertainty calibration, fairness, and efficient learning when labels are scarce or data cannot be pooled across hospitals. A central challenge is that strong results on internal benchmarks may not transfer across sites, devices, populations, or disease presentations, so patient-level splits and external validation remain essential.

Latest papers

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515 papers41 papers in the last four weeks, down 20% on the four weeks before. 0.3% of all new papers.

  1. WaRA: Wavelet Low-Rank Adaptation for Medical Image Classification

    Jun 25, 2025Moein Heidari, Yijin Huang, Yasamin Medghalchi +3Vision Foundation Model AdaptationLow-Rank Adaptation

    School of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada · Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China · Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada +2

  2. Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification

    Jun 23, 2025Ciro Listone, Aniello MuranoVariational AutoencodersSkin Lesion Classification

    University of Naples Federico II, Naples, Italy.

  3. Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification

    Feb 13, 2025Luisa Gallée, Meinrad Beer, Michael GötzMedical Image ClassificationExplainable Medical Image Analysis

    Experimental Radiology, Ulm University Medical Center, Germany · XAIRAD - Cooperation for Artificial Intelligence in Experimental Radiology, Germany · Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Germany +2

  4. Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images

    May 12, 2024Fatema Tuj Johora Faria, Mukaffi Bin Moin, Pronay Debnath +2Explainable Medical Image AnalysisRetinal Vessel Segmentation

    aDepartment of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh

  5. Performance of Machine Learning Classification in Sonomammogram Images using BI-RADS

    Nov 14, 2023Malitha Gunawardhana, Norbert ZolekMedical Image ClassificationUltrasound Imaging

    Auckland Bioengineering Institute University of Auckland New Zealand · Institute of Fundamental Technological Research Polish Academy of Sciences Poland