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What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation
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Unsupervised domain adaptation has attracted growing research attention on semantic segmentation. However, 1) most existing models cannot be directly applied into lesions transfer of medical images, due to the diverse appearances of same lesion among different datasets; 2) equal attention has been paid into all semantic representations instead of neglecting irrelevant knowledge, which leads to negative transfer of untransferable knowledge. To address these challenges, we develop a new unsupervised semantic transfer model including two complementary modules (i.e., T_D and T_F ) for endoscopic lesions segmentation, which can alternatively determine where and how to explore transferable domain-invariant knowledge between labeled source lesions dataset (e.g., gastroscope) and unlabeled target diseases dataset (e.g., enteroscopy). Specifically, T_D focuses on where to translate transferable visual information of medical lesions via residual transferability-aware bottleneck, while neglecting untransferable visual characterizations. Furthermore, T_F highlights how to augment transferable semantic features of various lesions and automatically ignore untransferable representations, which explores domain-invariant knowledge and in return improves the performance of T_D. To the end, theoretical analysis and extensive experiments on medical endoscopic dataset and several non-medical public datasets well demonstrate the superiority of our proposed model.
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“However, none of the current works study poisoning attacks on federated machine learning [11], [17], [18], where the training data are distributed across multiple devices”
From Data Poisoning Attacks on Federated Machine Learning (2022) · cites this paper as [11]
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Uni3DA: Universal 3D Domain Adaptation for Object Recognition
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Self-citationUnsupervised Video-Based Action Recognition With Imagining Motion and Perceiving Appearance
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Motion and Appearance Adaptation for Cross-domain Motion Transfer
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Self-citationAPAN: Across-Scale Progressive Attention Network for Single Image Deraining
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Self-citationUnderstanding and Mitigating Data Contamination in Deep Anomaly Detection: A Kernel-based Approach
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Weak Adaptation Learning: Addressing Cross-domain Data Insufficiency with Weak Annotator
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Cross-modality transfer learning with knowledge infusion for diabetic retinopathy grading
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Metadata
- Authors
- Dong, J., Cong, Y., Sun, G., Zhong, B., & Xu, X.
- Affiliations
- Shenyang Institute of Automation (CN)
- Huaqiao University (CN)
- University of Arkansas at Little Rock (US)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 4022-4031
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035260401
- Cited by
- 149
- References
- 49
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 20 · 2023: 33 · 2022: 36 · 2021: 39 · 2020: 9
- FWCI
- 11.02
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61533015, 61722311, U1613214, 61821005, 2019YFB1310300)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- unsupervised domain adaptation, semantic segmentation, medical image transmission, negative transfer, gastroscope, enteroscopy, cross-dataset transfer
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