Less is More: Selective reduction of CT data for self-supervised pre-training of deep learning models with contrastive learning improves downstream classification performance

被引：0

作者：

Wolf, Daniel ^{[1
,2
]}

Payer, Tristan ^{[1
]}

Lisson, Catharina Silvia ^{[2
]}

Lisson, Christoph Gerhard ^{[2
]}

Beer, Meinrad ^{[2
]}

Götz, Michael ^{[2
]}

Ropinski, Timo ^{[1
]}

机构：

[1] Visual Computing Research Group, Institute of Media Informatics, Ulm University, James-Franck-Ring, Ulm,89081, Germany

[2] Experimental Radiology Research Group, Department for Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert Einstein Allee, Ulm,89081, Germany

来源：

Computers in Biology and Medicine | 2024年 / 183卷

基金：

美国国家卫生研究院;

关键词：

Medical imaging - Self-supervised learning - Semi-supervised learning;

D O I：

10.1016/j.compbiomed.2024.109242

中图分类号：

学科分类号：

摘要：

引用

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