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Two-Stage Classification Method for MSI Status Prediction Based on Deep Learning Approach

التفاصيل البيبلوغرافية
العنوان: Two-Stage Classification Method for MSI Status Prediction Based on Deep Learning Approach
المؤلفون: Hyunseok Lee, Jihyun Seo, Giwan Lee, Jongoh Park, Doyeob Yeo, Ayoung Hong
المصدر: Applied Sciences, Vol 11, Iss 1, p 254 (2020)
بيانات النشر: MDPI AG, 2020.
سنة النشر: 2020
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: colorectal cancer, pathological image, MSI status, deep learning, segmentation, classification, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: Colorectal cancer is one of the most common cancers with a high mortality rate. The determination of microsatellite instability (MSI) status in resected cancer tissue is vital because it helps diagnose the related disease and determine the relevant treatment. This paper presents a two-stage classification method for predicting the MSI status based on a deep learning approach. The proposed pipeline includes the serial connection of the segmentation network and the classification network. In the first stage, the tumor area is segmented from the given pathological image using the Feature Pyramid Network (FPN). In the second stage, the segmented tumor is classified as MSI-L or MSI-H using Inception-Resnet-V2. We examined the performance of the proposed method using pathological images with 10× and 20× magnifications, in comparison with that of the conventional multiclass classification method where the tissue type is identified in one stage. The F1-score of the proposed method was higher than that of the conventional method at both 10× and 20× magnifications. Furthermore, we verified that the F1-score for 20× magnification was better than that for 10× magnification.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/11/1/254; https://doaj.org/toc/2076-3417
DOI: 10.3390/app11010254
URL الوصول: https://doaj.org/article/ddbc2816829044e7be12584883a2f4cd
رقم الأكسشن: edsdoj.bc2816829044e7be12584883a2f4cd
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app11010254