Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation
不同於GAN，對於此類遷移學習的目標是使得原始數據和目標數據的特徵沒有差別。原始標籤是0.目標是1，也就是D輸出的概率大就判別爲目標，對於前項，需要讓他更接近於目標，所以要讓D（SNET-S）變大，讓D（SNET-T變小）。同時這個損失爲了讓加強邊界的損失，在LOSS中加入了邊界的權重。
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