Newest papers

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

Samuel Halimi, Loic Themyr, Arnaud Abreu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29830 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

胸部X射线异常检测的深度学习模型仍然容易受到微妙的分布变化(例如,采集技术,与患者相关的因素和预处理)。传统的错误分析往往依赖于语义元数据或模型嵌入,这些模型可以掩盖低水平的信号变异,从而降低性能。在这项工作中,我们提出了一个以数据为中心的框架,用于自动故障模式发现,使用光谱分析。我们将图像投射到频率领域,并提取一个紧凑的资料,概括了信号能量在频率带上的分布。通过对这些光谱配置进行无监督的集群化,我们证明模型故障不是随机分布的,而是集中在特定的光谱集群中。这种方法有效地分离了“盲点”,使模型可靠性预测

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 unsupervised clustering on these spectral profiles, we demonstrate that model failures are not randomly distributed, but are strongly concentrated within specific spectral clusters. This method effectively isolates "blind spots", enabling the prediction of model reliability and the discovery of performance-degrading data slices without requiring ground-truth failure annotations.

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Topics: Computer science, Artificial intelligence, Reliability (semiconductor), Pattern recognition (psychology), Cluster analysis, Spectral clustering

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