We propose a novel gradient-based meta-learning scheme to tackle the challenges when deploying the model to a different medical center with the lack of labeled data. A pre-trained model is always suboptimal when deploying to different medical centers, where various protocols and scanners are used. Our method combines a 2D U-Net as a segmentor to generate segmentation maps and an adversarial network to learn from the shape prior in the meta-train and meta-test. Evaluation results on the public prostate MRI data and our HKU local database show that our approach outperformed the existing naive U-Net methods.
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