Re-weighting and Hierarchical Pre-training Boost 3D Medical Self-Supervised Representation-Reference-Cited by-同舟云学术

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Re-weighting and Hierarchical Pre-training Boost 3D Medical Self-Supervised Representation

Published:2022-11-26 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)
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Author:

Chen Shouyu¹,Wang Yin¹,Sun Ke²,Sun Xiwen³

Affiliation:

1. Tongji University,College of Electronics and Information Engineering,Department of Computer Science and Technology,Shanghai,China

2. Fudan University,Huashan Hospital,Department of Radiology,Shanghai,China

3. Tongji University School of Medicine,Shanghai Pulmonary Hospital,Department of Radiology,Shanghai,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10016307/10016308/10016411.pdf?arnumber=10016411

Reference31 articles.

1. Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

2. Delving into deep imbalanced regression;yang;International Conference on Machine Learning,2021

3. Bootstrap your own latent: A new approach to self-supervised learning;grill;arXiv preprint arXiv 2006 07529,2020

4. Adasyn: Adaptive synthetic sampling approach for imbalanced learning;he;2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence),2008

5. Momentum Contrast for Unsupervised Visual Representation Learning

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