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Smooth Deep Saliency
April 4, 2024, 4:45 a.m. | Rudolf Herdt, Maximilian Schmidt, Daniel Otero Baguer, Peter Maa{\ss}
cs.CV updates on arXiv.org arxiv.org
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 …
abstract arxiv cs.cv deep learning downsampling gradient hidden maps noise reduce samples tumors type work
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