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政大機構典藏 > 文學院 > 圖書資訊與檔案學研究所 > 期刊論文 > Item 140.119/150695

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/150695

Title:	Detecting microsatellite instability in colorectal cancer using Transformer-based colonoscopy image classification and retrieval
Authors:	羅崇銘 Lo, Chung-Ming;Jiang, Jeng-Kai;Lin, Chun-Chi
Contributors:	圖檔所
Date:	2024-01
Issue Date:	2024-04-10 16:26:53 (UTC+8)
Abstract:	Colorectal cancer (CRC) is a major global health concern, with microsatellite instability-high (MSI-H) being a defining characteristic of hereditary nonpolyposis colorectal cancer syndrome and affecting 15% of sporadic CRCs. Tumors with MSI-H have unique features and better prognosis compared to MSI-L and microsatellite stable (MSS) tumors. This study proposed establishing a MSI prediction model using more available and low-cost colonoscopy images instead of histopathology. The experiment utilized a database of 427 MSI-H and 1590 MSS colonoscopy images and vision Transformer (ViT) with different feature training approaches to establish the MSI prediction model. The accuracy of combining pre-trained ViT features was 84% with an area under the receiver operating characteristic curve of 0.86, which was better than that of DenseNet201 (80%, 0.80) in the experiment with support vector machine. The content-based image retrieval (CBIR) approach showed that ViT features can obtain a mean average precision of 0.81 compared to 0.79 of DenseNet201. ViT reduced the issues that occur in convolutional neural networks, including limited receptive field and gradient disappearance, and may be better at interpreting diagnostic information around tumors and surrounding tissues. By using CBIR, the presentation of similar images with the same MSI status would provide more convincing deep learning suggestions for clinical use.
Relation:	PLOS One, Vol.19, No.1, e0292277
Data Type:	article
DOI 連結:	https://doi.org/10.1371/journal.pone.0292277
DOI:	10.1371/journal.pone.0292277
Appears in Collections:	[圖書資訊與檔案學研究所] 期刊論文

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