Newest papers

Revealing Hidden Failure Modes in Chest X-ray Classification via Spectral Domain Analysis

Samuel Halimi, Loic Themyr, Arnaud Abreu · 2026 · arXiv

DOI: 10.48550/arXiv.2610.29830 · open-access PDF

Japanese

胸部X線異常検出のための深層学習モデルは、微妙な分布の変化(例えば、取得技術、患者関連の要因、およびプレプロセッサ)に脆弱である。伝統的なエラー分析はしばしば、低レベルの信号変異を隠すことができるセマンティックメタデータまたはモデル埋め込みに依存し、この作業では、スペクトル分析を使用して自動的な故障モード発見のためのデータ中心のフレームワークを提案します。

Machine translation (Meta M2M100); the English original follows.

Abstract

Deep learning models for chest X-ray anomaly detection remain vulnerable to subtle distributional shifts (e.g., acquisition technique, patient-related factors, and preprocessing). Traditional error analysis often relies on semantic metadata or model embeddings, which can mask low-level signal variations that degrade performance. In this work, we propose a data-centric framework for automated failure mode discovery using spectral analysis. We project images into the frequency domain and extract a compact profile summarizing the distribution of signal energy across frequency bands. By performing

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