Presentation information

Organized Session

Organized Session » OS-1

[2K5-OS-1a] 医療におけるAIの社会実装に向けて(1/2)

Wed. Jun 15, 2022 3:20 PM - 5:00 PM Room K (Room K)

オーガナイザ:小寺 聡(東京大学)[現地]、木村 仁星(東京大学)、小林 和馬(国立がん研究センター)、杉原 賢一(エムスリー)

4:00 PM - 4:20 PM

[2K5-OS-1a-03] Self-Supervised Contrastive Learning for Electrocardiograms to Detect Left Ventricular Systolic Dysfunction

〇Mitsuhiko Nakamoto1, Satoshi Kodera1, Hirotoshi Takeuchi1, Shinnosuke Sawano1, Susumu Katsushika1, Kota Ninomiya1, Hiroshi Akazawa1, Issei Komuro1 (1. Department of Cardiovascular Medicine, The University of Tokyo Hospital)


Keywords:self-supervised learning, contrastive learning, electrocardiography

Self-supervised learning has been demonstrated to be a powerful way to use unlabeled data in computer vision tasks. In this study, we propose a self-supervised pretraining approach to improve the performance of deep learning models that detect left ventricular systolic dysfunction from 12-lead electrocardiography data. We first pretrain an encoder that can extract rich features from unlabeled electro- cardiography data using self-supervised contrastive learning, and then fine-tune the model on the downstream dataset using the pretrained encoder. In experiments, our proposed approach achieved higher performance than the supervised baseline method, using only 28% of the labels used by the baseline method.

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