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

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 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

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

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 →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 · 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 · 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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