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다중 기관에서의 디지털 병리 암 분화도 예측을 위한 멀티 태스크 기반 단일 모델 학습
임종우(Lim Jong Woo),신상혁(Shin Sang Hyeok),강동연(Kang Dong Yeon),이주천(Jucheon Lee),이재웅(Jaeung Lee),곽진태(Jin Tae Kwak) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.11
In this study, we propose a single multi-task deep learning model for classifying digital pathology images from multiple organs based on the degree of cancer differentiation. For multi-organ cancer classification, there has been two major approaches in digital pathology. One is to develop a separate model per organ. Second is to employ an ensemble model to combine multiple models that were trained on different organs. Both approaches are time- and resource-inefficient. Herein, we propose a single multi-task model that simultaneously utilizes pathology images from multiple organs. Three digital pathology datasets, including colon, prostate, and gastric tissue images, are employed in this study. The experimental results demonstrate that the proposed approach is able to improve the overall cancer classification performance, which outperforms single organ models and ensemble models.
디지털 병리 대장암 진단을 위한 삼중항 손실을 이용한 심층 메트릭 학습
이재웅(Jae Ung Lee),김경은(Kyung Eun Kim),송보람(Bo Ram Song),이주천(Ju Cheon Lee),Vuong Thi Le Trinh,Wang Jiamu,Syed Farhan Abbas,곽진태(Jin Tae Kwak) 대한전자공학회 2022 대한전자공학회 학술대회 Vol.2022 No.11
In this paper, we propose a deep metric learning classification model for colon cancer grading which aims to learn feature vector similarity by distance comparisons. The proposed method is evaluated on >9,800 colorectal image patches. The experimental results show that the method achieves 87.85% accuracy and 0.8425 F1-score, suggesting that the proposed learning method can improve histopathological analysis of cancer grade classification in pathological images.
디지털 병리 대장암 분화도 예측을 위한 순서학습 기반 비전 트랜스포머 기술
이주천(Ju Cheon Lee),이재웅(Jae Ung Lee),Vuong Thi Le Trinh,Wang Jiamu,JiangKan,변근호(Keunho Byeon),정수민(sumin Jung),Anh Tien Nguyen,Bui Cao Doanh,곽진태(Jin Tae Kwak) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6
We propose a deep learning based digital pathology method that can classify colorectal cancers from digitized whole slide images. The conventional digital pathology methods approach cancer grading as a categorical classification problem, where the goal is to classify them into appropriate classes. However, in the case of cancer cells, the higher the grade or differentiation of each class, the poorer the condition of the cancer is, making simple categorical classification insufficient to address this issue. Therefore, in this paper, we formulate cancer grading as both categorical and ordinal classification problems and conduct two cancer grading tasks simultaneously. To achieve this, we build a deep learning model based on vision transformer and order learning. The proposed method is evaluated using a colorectal tissue dataset. Experimental results show that our method is able to accurately classify cancer grades and outperforms other competing models.