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AI models tackle rare disease classification in medical images · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Two research papers address the challenge of long-tailed distributions in medical image classification, where rare diseases have very few data samples. The first paper explores standard deep learning models with augmentation techniques to improve classification accuracy for rarer conditions, evaluating performance using metrics like F1 score and AUROC. The second paper introduces GazeLT, a novel approach that integrates human visual attention patterns into deep learning models to better capture both common and rare disease indicators in chest radiographs, demonstrating significant improvements over existing methods on large datasets. AI

IMPACT These research efforts aim to improve diagnostic accuracy for rare diseases, potentially leading to earlier detection and better patient outcomes in healthcare.

RANK_REASON Two academic papers published on arXiv discussing methods for long-tailed medical image classification.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI models tackle rare disease classification in medical images · 2 sources tracked

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Two academic papers published on arXiv discussing methods for long-tailed medical image classification.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Nathanael Ren, Saagar Arya ·

    Long-Tailed Medical Image Classification

    arXiv:2607.23883v1 Announce Type: cross Abstract: In this paper, we examine the difficulties of using standard techniques for medical image classification due to long-tailed distributions (wherein rarer conditions have very few samples) resulting in bias towards diagnosing common…

  2. arXiv cs.CV TIER_1 English(EN) · Moinak Bhattacharya, Gagandeep Singh, Shubham Jain, Prateek Prasanna ·

    GazeLT: Visual attention-guided long-tailed disease classification in chest radiographs

    arXiv:2508.09478v2 Announce Type: replace Abstract: In this work, we present GazeLT, a human visual attention integration-disintegration approach for long-tailed disease classification. A radiologist's eye gaze has distinct patterns that capture both fine-grained and coarser leve…

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