2022
DOI: 10.48550/arxiv.2205.09292
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Free Lunch for Surgical Video Understanding by Distilling Self-Supervisions

Abstract: 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-supervis… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2022
2022
2022
2022

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 30 publications
0
1
0
Order By: Relevance
“…Owing to more nonlinear combinations and hyperparameters, deep learning in large datasets tends to achieve good performance [10]. Because labeling medical image data is difficult, deep learning strategies based on semisupervised or self-supervised methods are gradually being applied [11,12]. Although semisupervised or self-supervised methods are not as effective as supervised learning, these two methods have great potential for future applications, because of the difficulty of medical data annotation.…”
Section: Introductionmentioning
confidence: 99%
“…Owing to more nonlinear combinations and hyperparameters, deep learning in large datasets tends to achieve good performance [10]. Because labeling medical image data is difficult, deep learning strategies based on semisupervised or self-supervised methods are gradually being applied [11,12]. Although semisupervised or self-supervised methods are not as effective as supervised learning, these two methods have great potential for future applications, because of the difficulty of medical data annotation.…”
Section: Introductionmentioning
confidence: 99%