다중 기관에서의 디지털 병리 암 분화도 예측을 위한 멀티 태스크 기반 단일 모델 학습

다국어 초록 (Multilingual Abstract)

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.

목차 (Table of Contents)

• Abstract
• I. 서론
• II. 본론
• III. 구현
• IV. 결론 및 향후 연구 방향
• 참고문헌