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학술지 Two-Stage Classification Method for MSI Status Prediction Based on Deep Learning Approach
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이현석, 서지현, 이기완, 박종오, 여도엽, 홍아영
Applied Sciences, v.11 no.1, pp.1-11
Colorectal cancer is one of the most common cancers with a high mortality rate. The deter-mination 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× magnifi-cations. Furthermore, we verified that the F1-score for 20× magnification was better than that for 10× magnification.
Classification, Colorectal cancer, Deep learning, MSI status, Pathological image, Segmentation
KSP 제안 키워드
Cancer tissue, Classification method, Colorectal Cancer, Conventional methods, F1-score, First stage, Learning approach, Microsatellite instability, Multiclass Classification, Pathological Image, Status prediction
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