고려대학교 · Medicine
Dinggang Shen 교수의 연구실은 의료 영상 분석 분야에서 인공지능, 특히 딥러닝과 트랜스포머 기반 기법을 활용한 정밀의료 기반 진단 기술 개발에 중점을 두고 있습니다. 주요 연구 방향은 뇌 영상 분석을 통한 알츠하이머병 조기 진단, 유방 초음파 및 폐 CT 영상에서의 악성 종양 진단 보조 시스템 개발, 그리고 영유아 뇌 어휘체계 구축 등입니다. 특히 이미지의 정확한 분할과 특징 추출에 의존하지 않는 내재적 노이즈 저항성 있는 신경망 아키텍처를 활용한 고도화된 컴퓨터 지원 진단 시스템 개발에 기여하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leadi
This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applic
We expect that the proposed infant 0-1-2 brain atlases would be significantly conducive to structural and functional studies of the infant brains. These atlases are publicly available in our website, http://bric.unc.edu/ideagroup/free-softwares/.
Transformers have dominated the field of natural language processing and have recently made an impact in the area of computer vision. In the field of medical image analysis, transformers have also been successfully used in to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. This paper aimed to promote awareness of the applications of transformers in medical image analysis. Specifically, we first provided an overview
Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimer's disease (AD)) and quantitative change (i.e., cognitive scores) at future time points, is important for early diagnosis of AD and for monitoring the disease progression. In this paper, we propose to predict future clinical changes of MCI patients by using both baseline and longitudinal multimodality data. To do this, we first develop a longitudina
