An empirical study on the use of visual explanation in kidney cancer detection

Abstract

In order to detect kidney cancer automatically from abdominal UCT (unenhanced CT) or CECT (contrastenhanced CT) images at an early stage, a promising approach is to use deep learning techniques with convolutional neural networks (CNNs). However, there still seem to be several challenges in detection of kidney cancer. For example, it is necessary to cope with the wide variety of abdominal CT images. In this paper, as an empirical study, we attempt to construct a CNN that detects kidney cancer well from abdominal CT images, with a special focus on how visual explanations produced by Gradient-weighted Class Activation Mapping (Grad-CAM) help us to construct such a CNN.

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