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New model anchors momentum to improve long-tailed chest X-ray classification

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new model called the Momentum-Anchored Multi-Scale Fusion Network to address class imbalance in chest X-ray classification. This model uses exponential moving averages to stabilize feature representations, preventing bias towards common conditions and improving performance on rare diseases. Tested on the ChestX-ray14 dataset, the method achieved an average AUC of 0.8682, showing significant gains for pathologies like Hernia and Pneumonia. AI

IMPACT Improves diagnostic accuracy for rare conditions in medical imaging, potentially leading to earlier detection and better patient outcomes.

RANK_REASON This is a research paper published on arXiv detailing a new model for medical image classification.

Read on arXiv cs.CV →

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

New model anchors momentum to improve long-tailed chest X-ray classification

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Duy Hoang Khuong, Duy Nguyen Huu, Ngu Huynh Cong Viet ·

    Momentum-Anchored Multi-Scale Fusion Model for Long-Tailed Chest X-Ray Classification

    arXiv:2605.02292v1 Announce Type: new Abstract: Chest X-ray classification suffers from severe class imbalance where gradient updates bias toward majority classes, causing feature drift and poor performance on rare but critical pathologies. We propose a Momentum-Anchored Multi-Sc…

  2. arXiv cs.CV TIER_1 English(EN) · Ngu Huynh Cong Viet ·

    Momentum-Anchored Multi-Scale Fusion Model for Long-Tailed Chest X-Ray Classification

    Chest X-ray classification suffers from severe class imbalance where gradient updates bias toward majority classes, causing feature drift and poor performance on rare but critical pathologies. We propose a Momentum-Anchored Multi-Scale Fusion Network that uses exponential moving …

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