Benchmarking saliency methods for chest X-ray interpretation
نویسندگان
چکیده
Abstract Saliency methods, which produce heat maps that highlight the areas of medical image influence model prediction, are often presented to clinicians as an aid in diagnostic decision-making. However, rigorous investigation accuracy and reliability these strategies is necessary before they integrated into clinical setting. In this work, we quantitatively evaluate seven saliency including Grad-CAM, across multiple neural network architectures using two evaluation metrics. We establish first human benchmark for chest X-ray segmentation a multilabel classification set-up, examine under what conditions might be more prone failure localizing important pathologies compared with expert benchmark. find (1) while Grad-CAM generally localized better than other evaluated all performed significantly worse benchmark, (2) gap localization performance between was largest were smaller size had shapes complex, (3) confidence positively correlated performance. Our work demonstrates several limitations methods must addressed can rely on them deep learning explainability imaging.
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ژورنال
عنوان ژورنال: Nature Machine Intelligence
سال: 2022
ISSN: ['2522-5839']
DOI: https://doi.org/10.1038/s42256-022-00536-x