政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/142128

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/142128

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政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/142128

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/142128

Title:	基於孿生網絡之正則化對比式遷移學習於醫療影像 Contrastive Transfer Learning for Regularization with Triplet Network on Medical Imaging
Authors:	游勤葑 Yu, Chin-Feng
Contributors:	邱淑怡 Chiu, Shu-I 游勤葑 Yu, Chin-Feng
Keywords:	黃斑部病變對比式學習遷移式學習正則化 Macular degeneration Contrastive learning Transfer learning Regularization
Date:	2022
Issue Date:	2022-10-05 09:15:57 (UTC+8)
Abstract:	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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Description:	碩士國立政治大學資訊科學系 110753205
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0110753205
Data Type:	thesis
DOI:	10.6814/NCCU202201567
Appears in Collections:	[Department of Computer Science ] Theses

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