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Free Lunch for Surgical Video Understanding by Distilling Self-Supervisions. (arXiv:2205.09292v1 [cs.CV])
May 20, 2022, 1:10 a.m. | Xinpeng Ding, Ziwei Liu, Xiaomeng Li
cs.CV updates on arXiv.org arxiv.org
Self-supervised learning has witnessed great progress in vision and NLP;
recently, it also attracted much attention to various medical imaging
modalities such as X-ray, CT, and MRI. Existing methods mostly focus on
building new pretext self-supervision tasks such as reconstruction,
orientation, and masking identification according to the properties of medical
images. However, the publicly available self-supervision models are not fully
exploited. In this paper, we present a powerful yet efficient self-supervision
framework for surgical video understanding. Our key insight is …
More from arxiv.org / cs.CV updates on arXiv.org
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