Smooth Deep Saliency
CoRR(2024)
Abstract
In this work, we investigate methods to reduce the noise in deep saliency
maps coming from convolutional downsampling, with the purpose of explaining how
a deep learning model detects tumors in scanned histological tissue samples.
Those methods make the investigated models more interpretable for
gradient-based saliency maps, computed in hidden layers. We test our approach
on different models trained for image classification on ImageNet1K, and models
trained for tumor detection on Camelyon16 and in-house real-world digital
pathology scans of stained tissue samples. Our results show that the
checkerboard noise in the gradient gets reduced, resulting in smoother and
therefore easier to interpret saliency maps.
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