Dr. Arnout Van Messem Profile

Dr. Arnout Van Messem

at Univ Gent

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Proceedings Article | 15 February 2021 Presentation + Paper

Towards a quantitative analysis of class activation mapping for deep learning-based computer-aided diagnosis

Hanul Kang, Ho-min Park, Yuju Ahn, Arnout Van Messem, Wesley De Neve

Proceedings Volume 11599, 115990M (2021) https://doi.org/10.1117/12.2580819

KEYWORDS: Image segmentation, Content addressable memory, Tumors, Quantitative analysis, Computer aided diagnosis and therapy, Medical imaging, Neuroimaging, Image processing, Image classification, Brain

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Class Activation Mapping (CAM) can be used to obtain a visual understanding of the predictions made by Convolutional Neural Networks (CNNs), facilitating qualitative insight into these neural networks when they are, for instance, used for the purpose of medical image analysis. In this paper, we investigate to what extent CAM also enables a quantitative understanding of CNN-based classification models through the creation of segmentation masks out of class activation maps, hereby targeting the use case of brain tumor classification. To that end, when a class activation map has been created for a correctly classified brain tumor, we additionally perform tumor segmentation by binarization of the aforementioned map, leveraging different methods for thresholding. In a next step, we compare this CAM-based segmentation mask to the segmentation ground truth, measuring similarity through the use of Intersection over Union (IoU). Our experimental results show that, although our CNN-based classification models have a similarly high accuracy between 86.0% and 90.8%, their generated masks are different. For example, our Modified VGG-16 model scores an mIoU of 12.2%, whereas AlexNet scores an mIoU of 2.1%. When comparing with the mIoU obtained by our U-Net-based models, which is between 66.6% and 67.3%, and where U-Net is a dedicated pixel-wise segmentation model, our experimental results point to a significant difference in terms of segmentation effectiveness. As such, the use of CAM for the purpose of proxy segmentation or as a ground truth segmentation mask generator comes with several limitations.

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