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

Login Paper Search My Schedule Paper Index Help

My ICIP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDBIO-1.2
Paper Title BRIDGING THE GAP BETWEEN OUTPUTS: DOMAIN ADAPTATION FOR LUNG CANCER IHC SEGMENTATION
Authors Li Diao, Shanghai Jiao Tong University, China; Haoyue Guo, Tongji University, China; Yue Zhou, Shanghai Jiao Tong University, China; Yayi He, Shanghai Pulmonary Hospital, China
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 Lung cancer is globally the most common cause of cancer-related death and immunotherapy is one of the most promising treatments for various cancers. Since there are some differences in characteristics between lung cancer immunohistochemistry (IHC) image datasets collected by different hospitals or laboratories, and relabeling new datasets is time-consuming and laborious, we thus propose an effective domain adaptation framework for IHC image segmentation. In our method, maximum mean discrepancy of segmentation outputs is extracted and combined with generative adversarial networks to bridge the gap between the outputs from source and target domains. To make the networks more instructive, pseudo target labels generated by the source model are employed. Meanwhile, adaptive hyperparameter tuning offers much flexibility for the training process. The proposed model has been evaluated over our own datasets and other public medical benchmarks, and a comparison with other recent works demonstrates its effectiveness and robustness. Furthermore, the model is exerted to extract quantitative and spatial features of conventional lung cancer IHC images, which can be utilized for overall survival (OS) and relapse-free survival (RFS) predictions.