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뇌졸중 환자의 뇌 병변 분할 영상 예측을 위한 딥러닝 모델 및 학습 방법
양현(Hyun Yang),정수민(Sumin Jung),Vuong Thi Le Trinh,Bui Cao Doanh,Wang Jiamu,노홍기(Honggee Roh),김현정(Hyunjeong Kim),곽진태(Jin Tae Kwak) 대한전자공학회 2024 대한전자공학회 학술대회 Vol.2024 No.6
In this study, we investigate deep learning models for an effective and efficient lesion segmentation in 3D brain diffusion weighted images (DWI). For image segmentation, convolution neural networks (CNN) and Transformer-based models are widely used. CNNs excel at extracting local features, while Transformer-based models excel at extracting global features. Herein, we employ 2 CNN models (3D-Unet and 3D-UNet++) and 2 Transformerbased models (3D-MobileViT and 3D-SwinUNetR) for brain lesion segmentation. To evaluate the four models, DWI and ADC (apparent diffusion coefficient) of 651 brain stroke patients are used as train, validation, and test set in this study. The experimental results demonstrate that deep learning models are able to successfully segment stroke lesion but their performance varies depending on the size and frequency of the lesion among patients.
디지털 병리 대장암 분화도 예측을 위한 순서학습 기반 비전 트랜스포머 기술
이주천(Ju Cheon Lee),이재웅(Jae Ung Lee),Vuong Thi Le Trinh,Wang Jiamu,JiangKan,변근호(Keunho Byeon),정수민(sumin Jung),Anh Tien Nguyen,Bui Cao Doanh,곽진태(Jin Tae Kwak) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6
We propose a deep learning based digital pathology method that can classify colorectal cancers from digitized whole slide images. The conventional digital pathology methods approach cancer grading as a categorical classification problem, where the goal is to classify them into appropriate classes. However, in the case of cancer cells, the higher the grade or differentiation of each class, the poorer the condition of the cancer is, making simple categorical classification insufficient to address this issue. Therefore, in this paper, we formulate cancer grading as both categorical and ordinal classification problems and conduct two cancer grading tasks simultaneously. To achieve this, we build a deep learning model based on vision transformer and order learning. The proposed method is evaluated using a colorectal tissue dataset. Experimental results show that our method is able to accurately classify cancer grades and outperforms other competing models.