Bridging The Gap Between Outputs: Domain Adaptation For Lung Cancer Ihc Segmentation

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.