PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation

Understand

Deep convolutional networks have demonstrated the state-of-the-art performance on various medical image computing tasks.

• Leveraging images from different modalities for the same analysis task holds clinical benefits.
• However, the generalization capability of deep models on test data with different distributions remain as a major challenge.
• In this paper, we propose the PnPAdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e.g., MRI and CT.