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Asan Medical Information System for Healthcare Quality Improvement
류현정,김우성,이재호,민성우,김선자,이영수,이영하,남상우,어기승,서숙경,남미현 대한의료정보학회 2010 Healthcare Informatics Research Vol.16 No.3
Objectives: This purpose of this paper is to introduce the status of the Asan Medical Center (AMC) medical information system with respect to healthcare quality improvement. Methods: Asan Medical Information System (AMIS) is projected to become a completely electronic and digital information hospital. AMIS has played a role in improving the health care quality based on the following measures: safety, effectiveness, patient-centeredness, timeliness, efficiency, privacy, and security. Results: AMIS consisted of several distinctive systems: order communication system, electronic medical record, picture archiving communication system, clinical research information system, data warehouse, enterprise resource planning, IT service management system, and disaster recovery system. The most distinctive features of AMIS were the high alert-medication recognition & management system, the integrated and severity stratified alert system, the integrated patient monitoring system, the perioperative diabetic care monitoring and support system, and the clinical indicator management system. Conclusions: AMIS provides IT services for AMC, 7 affiliated hospitals and over 5,000 partners clinics, and was developed to improve healthcare services. The current challenge of AMIS is standard and interoperability. A global health IT strategy is needed to get through the current challenges and to provide new services as needed.
공간 적응적 비정규화 방식 기반 단일 영상 내 그림자 제거 기법
류현정,최윤식 한국통신학회 2022 韓國通信學會論文誌 Vol.47 No.4
Shadow removal from a single-image has been a significant issue in image processing and many computer vision areas. This paper proposes a novel network based on the conditional generative adversarial network scheme without requiring additional shadow detection process to remove shadows. The proposed structure also utilizes a spatially adaptive de-normalization method to prevent the input image information loss caused by various normalization layers in the neural network. From the various experiments related to shadow removal using authorized datasets, it is confirmed that the proposed network shows at least 5dB higher performance in PSNR, compared to the state of the arts neural network based methodologies. 단일 영상 내 그림자 제거는 영상 처리 및 많은 컴퓨터 시각 분야에서 중요한 문제이다. 이 논문에서는 이러한그림자를 제거하기 위하여, 추가적인 그림자 감지를 필요로 하지 않는 조건부 적대적 생성 신경망 기반의 새로운신경망 구조를 제안한다. 제안하는 구조에서는, 신경망 내 다양하게 존재하는 정규화층으로 인해 발생되는, 입력영상 데이터의 정보 손실 문제를 방지하기 위해 공간 적응적 비정규화 방식을 활용하였고, 그림자 제거와 관련된공인 데이터 세트를 활용한 다양한 실험들로부터, 제안된 기법이 기존의 신경망 기반의 최신 방법론들과 비교하여적어도 5dB 높은 PSNR 성능을 가지는 것을 확인할 수 있다.