Conditional Domain Adaptation GANs for Biomedical Image Segmentation
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Due to visual differences in biomedical image datasets acquired using distinct digitization techniques, Transfer Learning is an important step for improving the generalization capabilities of Neural Networks in this area. Despite succeeding in classification tasks, most Domain Adaptation strategies face serious limitations in segmentation. Therefore, improving on previous Image Translation networks, we propose a Domain Adaptation method for biomedical image segmentation based on adversarial networks that can learn from both unlabeled and labeled data. Our experimental procedure compares our method using several domains, datasets, segmentation tasks and baselines, performing quantitative and qualitative comparisons of the proposed method with baselines. The proposed method shows consistently better results than the baselines in scarce label scenarios, often achieving Jaccard values greater than 0.9 and adequate segmentation quality in most tasks and datasets.
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