Can saliency maps flag chest radiograph classifier errors at a new hospital? An external validation study
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Aashish Chapagai, Angad Singh Gurtatta, Erik Cambria · arXiv: 2611.05011v1 · Summary page
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A convincing heat map is not an error alarm
Saliency maps highlight image regions that influence a classifier’s output, offering an apparently intuitive way to inspect medical AI. But an external validation study finds little support for using them to flag individual mistakes at unfamiliar hospitals. Researchers trained a convolutional network and a vision transformer on NIH ChestX-ray14, then tested them at the training hospital and external sites in the USA and Vietnam, using radiologist-consensus labels. They measured whether attribution concentrated within an anatomical region helped detect errors beyond the model’s confidence. Adding that measure improved the error detector’s area under the curve by no more than 0.011. For the convolutional network, its relationship with correctness even reversed between hospitals. The practical distinction is important: an explanation that looks anatomically plausible is not necessarily a reliable warning system.
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