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

Masaya Takahashi; Yoshitaka Kameya; Keiichi Yamada; Kazuhiro Hotta; Tomoichi Takahashi; Naoto Sassa; Shingo Iwano; Tokunori Yamamoto

doi:10.1117/12.2573167

12 June 2020 An empirical study on the use of visual explanation in kidney cancer detection

Masaya Takahashi, Yoshitaka Kameya, Keiichi Yamada, Kazuhiro Hotta, Tomoichi Takahashi, Naoto Sassa, Shingo Iwano, Tokunori Yamamoto

Author Affiliations +

Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 115190A (2020) https://doi.org/10.1117/12.2573167
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan

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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Masaya Takahashi, Yoshitaka Kameya, Keiichi Yamada, Kazuhiro Hotta, Tomoichi Takahashi, Naoto Sassa, Shingo Iwano, and Tokunori Yamamoto "An empirical study on the use of visual explanation in kidney cancer detection", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 115190A (12 June 2020); https://doi.org/10.1117/12.2573167

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