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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.