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arXiv Machine Learning By Shreyash Arya, Shashank Agnihotri, Marcel Kleinmann, Bernt Schiele, Margret Keuper

Faithful, Interpretable Chest X-ray Diagnosis with Artifact-free B-cos Networks

Read the original on arXiv Machine Learning →

The paper presents artifact‑free B‑cos networks for chest X‑ray diagnosis, enhancing the original B‑cos architecture with anti‑aliasing techniques (ASAP and BlurPool) to eliminate aliasing artifacts in explanation maps. These modified networks, “B-cos_ASAP” and “B-cos_BP”, maintain competitive diagnostic performance while providing faithful, class‑specific contribution maps without post‑hoc explanations. Experiments on chest X‑ray datasets confirm that the new models deliver strong predictive accuracy and clear, artifact‑free explanations suitable for clinical use in multi‑class and multi‑label scenarios.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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