Exploring Backdoor Attacks in Off-the-Shelf Unsupervised Domain Adaptation for Securing Cardiac MRI-Based Diagnosis-Reference-Cited by-同舟云学术

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Exploring Backdoor Attacks in Off-the-Shelf Unsupervised Domain Adaptation for Securing Cardiac MRI-Based Diagnosis

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

Liu Xiaofeng¹,Xing Fangxu¹,Gaggin Hanna²,Kuo C.-C. Jay³,El Fakhri Georges⁴,Woo Jonghye¹

Affiliation:

1. Massachusetts General Hospital and Harvard Medical School,Dept. of Radiology,Boston,MA,USA

2. Massachusetts General Hospital and Harvard Medical School,Dept. of Medicine,Boston,MA,USA

3. University of Southern California,Dept. of Electrical and Computer Engineering,Los Angeles,CA,USA

4. Yale University,Dept. of Radiology and Biomedical Imaging,New Haven,CT,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10635099/10635102/10635403.pdf?arnumber=10635403

Reference19 articles.

1. Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives

2. Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation

3. Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation;Liang

4. Memory consistent unsupervised off-the-shelf model adaptation for source-relaxed medical image segmentation

5. Explainability matters: Backdoor attacks on medical imaging;Nwadike

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