Mlip: Medical Language-Image Pre-Training With Masked Local Representation Learning-Reference-Cited by-同舟云学术

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Mlip: Medical Language-Image Pre-Training With Masked Local Representation Learning

Published:2024-05-27 Issue: Volume:139 Page:1-5
ISSN:
Container-title:2024 IEEE International Symposium on Biomedical Imaging (ISBI)
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Author:

Liu Jiarun¹,Zhou Hong-Yu²,Li Cheng¹,Huang Weijian¹,Yang Hao¹,Liang Yong³,Shi Guangming³,Zheng Hairong¹,Wang Shanshan¹

Affiliation:

1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences,Paul C. Lauterbur Research Center for Biomedical Imaging,Shenzhen,China

2. The University of Hong Kong,Department of Computer Science,Pokfulam,China

3. Peng Cheng Laboratory,Shenzhen,China

Funder

National Natural Science Foundation of China

Peng Cheng Laboratory

Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10635099/10635102/10635357.pdf?arnumber=10635357

Reference20 articles.

1. Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning

2. Generalized radiograph representation learning via cross-supervision between images and free-text radiology reports

3. Annotation-efficient deep learning for automatic medical image segmentation

4. Sparse annotation learning for dense volumetric MR image segmentation with uncertainty estimation

5. Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing the vision–language foundation model with key semantic knowledge-emphasized report refinement;Medical Image Analysis;2024-10

2. Enhancing representation in radiography-reports foundation model: a granular alignment algorithm using masked contrastive learning;Nature Communications;2024-09-02

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