在此篇論文中，我們針對眼底攝影 ( Color Fundus Photography）醫療影像提出了一個新穎的遷移式學習架構，稱為基於孿生網絡之正則化對比式遷移學習（Contrastive Transfer Learning for Regularization with Triplet Network），CTLRT，在 CTLRT 中包含三種對比式正則化損失項且結合了遷移式學習的骨架，我們在三種眼底攝影資料集且多種遷移式學習骨架下表明 CTLRT 不只擁有比傳統的遷移式學習更高的準確
度，並且透過我們設計的對比式正則化損失減緩複雜模型帶來的過擬
合效應，提高了模型的泛化能力，且經由可視化模型關注的區域說明
了 CTLRT 確實能正確的關注變病的區域。

This paper focuses on Color Fundus Photography and proposes a novel transfer learning architecture called Contrastive Transfer Learning for Regularization with Triplet Network (CTLRT). CTLRT contains three kinds of contrastive regularization loss terms and combines the backbone of transfer learning. We use three fundus photography datasets and multiple transfer backbones. The following shows that CTLRT not only has higher accuracy than traditional transfer learning but also mitigates the overfitting effect brought by complex models through our designed contrastive regularization
loss and improves the model’s generalization ability. Visualizing the area where model interest shows that CTLRT correctly focuses on the diseased site.

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