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Smooth Deep Saliency

2024-04-02 20:15:43
Rudolf Herdt, Maximilian Schmidt, Daniel Otero Baguer, Peter Maaß

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.

Abstract (translated)

在这项研究中,我们研究了如何减少来自卷积下采样的深度突出度图中的噪声,以解释深度学习模型如何通过扫描组织样本检测肿瘤。这些方法使得研究中的模型对于基于梯度的突出度图更加可解释,该突出度图是在隐藏层计算的。我们在ImageNet1K上训练的不同模型以及为肿瘤检测在Camelyon16和玻片组织样本的体内真实世界数字病理学扫描上训练的模型上测试我们的方法。我们的结果表明,梯度中的检查员噪声减少,导致更平滑,因此更容易解释的突出度图。

URL

https://arxiv.org/abs/2404.02282

PDF

https://arxiv.org/pdf/2404.02282.pdf


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