췌장 관 선암종의 디지털 병리이미지에서 AI 활용의 임상적의의

다국어 초록 (Multilingual Abstract)

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.

목차 (Table of Contents)

• ABSTRACT
• Ⅰ. 서론
• Ⅱ. 본론
• Ⅲ. 임상적의의
• Ⅳ. 결론
• REFERENCES