Enhancing diagnostic deep learning via self-supervised pretraining on large-scale, unlabeled non-medical images

被引：0

作者：

Soroosh Tayebi Arasteh

Leo Misera

Jakob Nikolas Kather

Daniel Truhn

Sven Nebelung

机构：

[1] University Hospital RWTH Aachen,Department of Diagnostic and Interventional Radiology

[2] Faculty of Medicine and University Hospital Carl Gustav Carus Dresden,Institute and Polyclinic for Diagnostic and Interventional Radiology

[3] Technische Universität Dresden,Else Kröner Fresenius Center for Digital Health

[4] Technische Universität Dresden,Department of Medicine III

[5] University Hospital RWTH Aachen,Medical Oncology, National Center for Tumor Diseases (NCT)

[6] University Hospital Heidelberg,undefined

来源：

European Radiology Experimental | / 8卷

关键词：

Artificial intelligence; Deep learning; Medical image processing; Radiography (thoracic); Unsupervised machine learning;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

• Validated on over 800,000 chest radiographs from 6 datasets and 20 imaging findings, a self-supervised pretraining on non-medical images outperformed ImageNet-based supervised pretraining.

引用

共 50 条

[21] BrainMass: Advancing Brain Network Analysis for Diagnosis with Large-scale Self-Supervised Learning
Yang Y.
Ye C.
Su G.
Zhang Z.
Chang Z.
Chen H.
Chan P.
Yu Y.
Ma T.
IEEE Transactions on Medical Imaging, 2024, 43 (11) : 1 - 1
[22] Self-Supervised Graph Transformer on Large-Scale Molecular Data
Rong, Yu
Bian, Yatao
Xu, Tingyang
Xie, Weiyang
Wei, Ying
Huang, Wenbing
Huang, Junzhou
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 2020, 33
[23] LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching
Nguyen, Duy M. H.
Nguyen, Hoang
Diep, Nghiem T.
Pham, Tan N.
Cao, Tri
Nguyen, Binh T.
Swoboda, Paul
Ho, Nhat
Albarqouni, Shadi
Xie, Pengtao
Sonntag, Daniel
Niepert, Mathias
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
[24] Fast and scalable search of whole-slide images via self-supervised deep learning
Chengkuan Chen
Ming Y. Lu
Drew F. K. Williamson
Tiffany Y. Chen
Andrew J. Schaumberg
Faisal Mahmood
Nature Biomedical Engineering, 2022, 6 : 1420 - 1434
[25] Fast and scalable search of whole-slide images via self-supervised deep learning
Chen, Chengkuan
Lu, Ming Y.
Williamson, Drew F. K.
Chen, Tiffany Y.
Schaumberg, Andrew J.
Mahmood, Faisal
NATURE BIOMEDICAL ENGINEERING, 2022, 6 (12) : 1420 - +
[26] Efficient Medical Image Assessment via Self-supervised Learning
Huang, Chun-Yin
Lei, Qi
Li, Xiaoxiao
DATA AUGMENTATION, LABELLING, AND IMPERFECTIONS (DALI 2022), 2022, 13567 : 102 - 111
[27] Self-supervised pretraining enables deep learning-based classification of AMD with fewer annotations
Holland, Robbie
Menten, Martin Joseph
Leingang, Oliver
Bogunovic, Hrvoje
Hagag, Ahmed M.
Kaye, Rebecca
Riedl, Sophie
Traber, Ghislaine
Fritsche, Lars
Prevost, Toby
Scholl, Hendrik P.
Schmidt-Erfurth, Ursula
Sivaprasad, Sobha
Rueckert, Daniel
Lotery, Andrew J.
INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2022, 63 (07)
[28] SSGait: enhancing gait recognition via semi-supervised self-supervised learning
Xi, Hao
Ren, Kai
Lu, Peng
Li, Yongqiang
Hu, Chuanping
APPLIED INTELLIGENCE, 2024, 54 (07) : 5639 - 5657
[29] Aggregative Self-supervised Feature Learning from Limited Medical Images
Zhu, Jiuwen
Li, Yuexiang
Ding, Lian
Zhou, S. Kevin
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT VIII, 2022, 13438 : 57 - 66
[30] Localized Region Contrast for Enhancing Self-supervised Learning in Medical Image Segmentation
Yan, Xiangyi
Naushad, Junayed
You, Chenyu
Tang, Hao
Sun, Shanlin
Han, Kun
Ma, Haoyu
Duncan, James S.
Xie, Xiaohui
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2023, PT II, 2023, 14221 : 468 - 478

← 1 2 3 4 5 →