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arXiv · 2304.13192

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

Siddhartha Kapuria·Tarunraj G. Mohanraj·Nethra Venkatayogi·Ozdemir Can Kara·Yuki Hirata·Patrick Minot·Ariel Kapusta·Naruhiko Ikoma·Farshid Alambeigi
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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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Siddhartha Kapuria, Tarunraj G. Mohanraj, Nethra Venkatayogi, Ozdemir Can Kara, Yuki Hirata, Patrick Minot, Ariel Kapusta, Naruhiko Ikoma, Farshid Alambeigi. 2023-04-25. Towards Reliable Colorectal Cancer Polyps Classification via Vision Based Tactile Sensing and Confidence-Calibrated Neural Networks. https://arxiv.org/abs/2304.13192

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