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Abstract #4107

Few-shot Meta-learning with Adversarial Shape Prior for Zonal Prostate Segmentation on T2 Weighted MRI

Han Yu1, Varut Vardhanabhuti1, and Peng Cao1
1The University of Hong Kong, Hong Kong, Hong Kong

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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