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췌장 관 선암종의 디지털 병리이미지에서 AI 활용의 임상적의의
김종광(Jongkwang Kim),배수목(Sumok Bae),윤성미(Seong-Mi Yoon),Ho Young Chung,Myungsoo Kim,정성문(Sungmoon Jeong) 한국정보통신학회 2024 한국정보통신학회논문지 Vol.28 No.1
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.
온간 성형 하에서 A1 합금 분말의 정밀정형에 대한 유한요소해석
김기태(KiTae Kim),양훈철(HoonChul Yang),김종광(JongKwang Kim) 대한기계학회 2003 대한기계학회 춘추학술대회 Vol.2003 No.4
A finite element analysis for near-net-shape forming of Al6061 powder was performed under warm<br/> pressing. The advantages of warm compaction by rubber isostatic pressing were discussed to obtain parts with<br/> better density distributions. To simulate densification and deformed shape of a powder compact during warm<br/> pressing, the elastoplastic constitutive equation based on yield function of Shima-Oyane was implemented<br/> into a finite element program(ABAQUS). The hyperelastic constitutive equation based on the Ogden strain<br/> energy potential was employed to analyze nonlinear elastic response of rubber. Finite element results were<br/> compared with experimental data for Al6061 powder compacts under warm pressing.