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16 March 2020 Visualising decision-reasoning regions in computer-aided pathological pattern diagnosis of endoscytoscopic images based on CNN weights analysis
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Abstract
Purpose of this paper is to present a method for visualising decision-reasoning regions in computer-aided pathological pattern diagnosis of endocytoscopic images. Endocytoscope enables us to perform direct observation of cells and their nuclei on the colon wall at maximum 500-times ultramagnification. For this new modality, computer-aided pathological diagnosis system is strongly required for the support of non-expert physicians. To develop a CAD system, we adopt convolutional neural network (CNN) as the classifier of endocytoscopic images. In addition to this classification function, based on CNN weights analysis, we develop a filter function that visualises decision-reasoning regions on classified images. This visualisation function helps novice endocytoscopists to develop their understanding of pathological pattern on endocytoscopic images for accurate endocytoscopic diagnosis. In numerical experiment, our CNN model achieved 90 % classification accuracy. Furthermore, experimental results show that decision-reasoning regions suggested by our filter function contain characteristic pit patterns in real endocytoscopic diagnosis.
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Hayato Itoh, Zhongyang Lu, Yuichi Mori, Masashi Misawa, Masahiro Oda, Shin-ei Kudo, and Kensaku Mori "Visualising decision-reasoning regions in computer-aided pathological pattern diagnosis of endoscytoscopic images based on CNN weights analysis", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131438 (16 March 2020); https://doi.org/10.1117/12.2549532
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