2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDBIO-1.6
Paper Title FEATURE DISENTANGLEMENT FOR CROSS-DOMAIN RETINA VESSEL SEGMENTATION
Authors Jie Wang, Chaoliang Zhong, Cheng Feng, Jun Sun, Fujitsu R&D Center, Co., LTD, China; Yasuto Yokota, Fujitsu Laboratories, Japan
SessionBIO-1: Biomedical Signal Processing 1
LocationArea C
Session Time:Monday, 20 September, 13:30 - 15:00
Presentation Time:Monday, 20 September, 13:30 - 15:00
Presentation Poster
Topic Biomedical Signal Processing: Medical image analysis
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Domain shift is regarded as a key factor affecting the robustness of many models. Recently, unsupervised auxiliary learning (e.g., input reconstruction) has been proposed to improve the model’s domain transferability and alleviate cross-domain performance degradation; however, in the paradigm of existing approaches, the features extracted from various tasks are shared, which mixes the domain-invariant features from the main task and domain-specific feature from the auxiliary task, leading to an imperfect learning. To solve this problem, we propose a novel unsupervised domain adaptation method - the Disentangled Reconstruction Neural Network (DRNN) - for cross-domain retina vessel segmentation. DRNN leverages two tandem nets and disentangles the domain-invariant features and the domain-specific features in the multi-task learning process. We perform extensive experiments on public retina datasets and our proposed DRNN outperforms the competitors by a significant margin to achieve state-of-the-art results pertaining to retina vessel segmentation.