The DOI matches its Crossref record; 47 of the 49 references it cites were found in a registry or an index.
What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation
Open accessAbstract
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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Cites
Deep Residual Learning for Image Recognition
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
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GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433
A theory of learning from different domains
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Vaughan, J. W. (2010) · Machine Learning · doi:10.1007/s10994-009-5152-4 · Cited by 3,515
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Hoffman, J., Tzeng, E., Park, T., Zhu, J.-Y., Isola, P., Saenko, K., Efros, A. A., & Darrell, T. (2017) · arXiv · doi:10.48550/arxiv.1711.03213 · Cited by 2,656
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Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
Saito, K., Watanabe, K., Ushiku, Y., & Harada, T. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00392 · Cited by 2,328
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Transfer adaptation learning: A decade survey
Zhang, L., & Gao, X. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3183326 · Cited by 314
Data Poisoning Attacks on Federated Machine Learning
Sun, G., Cong, Y., Dong, J., Wang, Q., Lyu, L., & Liu, J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3128646 · Cited by 310
Self-citationVLP: A Survey on Vision-language Pre-training
Chen, F., Zhang, D., Han, M., Chen, X.-Y., Shi, J. C., Xu, S., & Xu, B. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1369-5 · Cited by 330
Addressing Class Imbalance in Federated Learning
Wang, L., Xu, S., Wang, X., & Zhu, Q. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i11.17219 · Cited by 306
Federated Class-Incremental Learning
Dong, J., Wang, L., Fang, Z., Sun, G., Xu, S., Wang, X., & Zhu, Q. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00992 · Cited by 279
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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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