Self-Supervised Multimodal Fusion Network for Knee Osteoarthritis Severity Grading-Reference-Cited by-同舟云学术

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Self-Supervised Multimodal Fusion Network for Knee Osteoarthritis Severity Grading

Published:2023-11-28 Issue: Volume:8 Page:57-64
ISSN:
Container-title:2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
language:
Short-container-title:

Author:

Wu Wenhua¹,Hu Kun¹,Yue Wenxi¹,Li Wei¹,Simic Milena²,Li Changyang³,Xiang Wei⁴,Wang Zhiyong¹

Affiliation:

1. The University of Sydney,School of Computer Science,Camperdown,NSW,Australia

2. The University of Sydney,Faculty of Medicine and Health,Camperdown,NSW,Australia

3. Sydney Polytechnic Institute Pty Ltd,Sydney,NSW,Australia

4. La Trobe University,School of Computing, Engineering & Mathematical Sciences

Funder

Australian Research Council

Health

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10410651/10410903/10410966.pdf?arnumber=10410966

Reference27 articles.

1. Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity Using Convolutional Neural Networks

2. Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss

3. A simple framework for contrastive learning of visual representations;Chen

4. Plain Radiography and Magnetic Resonance Imaging Diagnostics in Osteoarthritis: Validated Staging and Scoring

5. Knee Osteoarthritis Classification Using 3D CNN and MRI

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