췌장 관 선암종의 디지털 병리이미지에서 AI 활용의 임상적의의

김종광; 배수목; 윤성미; Ho Young Chung; Myungsoo Kim; 정성문

doi:10.6109/jkiice.2024.28.1.106

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자료유형: 학술저널

저자정보: 김종광 (Kyungpook National University) 배수목 (Kyungpook National University) 윤성미 (ORTHOTECH) Ho Young Chung (Kyungpook National University) Myungsoo Kim (Kyungpook National University) 정성문 (Kyungpook National University)

저널정보: 한국정보통신학회 한국정보통신학회논문지 한국정보통신학회논문지 제28권 제1호

발행연도: 2024.1

수록면: 106 - 109 (4page)

DOI: 10.6109/jkiice.2024.28.1.106

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Pancreatic Ductal Adenocarcinoma (PDAC) is the most common and deadly form of pancreatic cancer. Currently, histopathological diagnosis and prognosis of PDAC are time-consuming and labor-intensive for pathologists. Recent advances in pathological AI research aim to alleviate this. We accumulated training data, distinguishing PDAC areas in Whole Slide Images (WSIs) based on medical findings. Using this data, we trained a deep convolutional neural network for supervised learning to automatically interpret PDAC areas. The AI model achieved high Dice scores and, by visualizing the segmentation results of the predicted histological images, validated that PDAC diagnosis and identification of associated regions are automatically possible, similar to pathologists. Additionally, the AI model, which showed high specificity, suggests its potential as a co-pilot for pathological diagnosis and annotation.

#Pathologist #PDAC #Supervised learning #WSI

참고문헌 (9)

참고문헌 신청

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