Towards Reliable Colorectal Cancer Polyps Classification via Vision Based Tactile Sensing and Confidence-Calibrated Neural Networks

• URL: http://arxiv.org/abs/2304.13192v1
• Date: Tue, 25 Apr 2023 23:18:13 GMT
• Title: Towards Reliable Colorectal Cancer Polyps Classification via Vision Based Tactile Sensing and Confidence-Calibrated Neural Networks
• Authors: Siddhartha Kapuria, Tarunraj G. Mohanraj, Nethra Venkatayogi, Ozdemir Can Kara, Yuki Hirata, Patrick Minot, Ariel Kapusta, Naruhiko Ikoma, and Farshid Alambeigi
• Abstract summary: We develop a residual neural network to address its over-confident outputs for CRC polyps classification. We introduce noise and blur to the obtained textural images of the vision-based tactile sensing (VS-TS) system and test the model's reliability for non-ideal inputs.
• Score: 1.4518956926610687
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this study, toward addressing the over-confident outputs of existing artificial intelligence-based colorectal cancer (CRC) polyp classification techniques, we propose a confidence-calibrated residual neural network. Utilizing a novel vision-based tactile sensing (VS-TS) system and unique CRC polyp phantoms, we demonstrate that traditional metrics such as accuracy and precision are not sufficient to encapsulate model performance for handling a sensitive CRC polyp diagnosis. To this end, we develop a residual neural network classifier and address its over-confident outputs for CRC polyps classification via the post-processing method of temperature scaling. To evaluate the proposed method, we introduce noise and blur to the obtained textural images of the VS-TS and test the model's reliability for non-ideal inputs through reliability diagrams and other statistical metrics.

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