최진욱 교수
JinWook Choi
서울대학교 의공학교실 · 컴퓨터과학
연구실 소개
최진욱 교수의 연구실은 의료 영상 분석과 모바일 헬스 정보 시스템을 중심으로, 딥러닝 기반의 골다공로시스 조기 진단, 환자 데이터 통합 및 접근성 향상, 그리고 저해상도 및 저프레임레트의 깊이 영상 초해상도 복원 기술에 대한 연구를 수행하고 있습니다. 특히, 패널로닉 레이저 영상과 임상 기록에서의 생물학적 마커 추출을 통해 정밀의료와 원격의료의 실현 가능성을 탐색하고 있습니다. 연구는 임상 현장과의 융합을 통해 실제 환자 진료에 기여할 수 있는 실용적이고 정밀한 기술 개발을 목표로 합니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15<b>:</b> Dental panoramic radiographs (DPRs) provide information required to potentially evaluate bone density changes through a textural and morphological feature analysis on a mandible. This study aims to evaluate the discriminating performance of deep convolutional neural networks (CNNs), employed with various transfer learning strategies, on the classification of specific features of osteoporosis in DPRs. For objective labeling, we collected a dataset containing 680 images from different pat
Patient clinical data are distributed and often fragmented in heterogeneous systems, and therefore the need for information integration is a key to reliable patient care. Once the patient data are orderly integrated and readily available, the problems in accessing the distributed patient clinical data, the well-known difficulties of adopting a mobile health information system, are resolved. This paper proposes a mobile clinical information system (MobileMed), which integrates the distributed and
Our study demonstrated that the performance of the XGB model using initial information at ED triage for predicting patients in need of critical care outperformed the conventional model with KTAS.
Our proposed approach for biomarker data extraction addresses key limitations regarding data representation and can handle reports prepared in the clinical setting, which often contain incomplete sentences, typographical errors, and inconsistent formatting.
This paper proposes a novel framework for up-conversion of depth video resolution both in spatial and in time domain. Time-of-flight (TOF) sensors are widely used in computer vision fields. Although TOF sensors provide depth video in real time, there are some problems in a sense that it provides a low resolution and a low frame-rate depth video. We propose a cheaper solution that enhances depth video obtained by TOF sensor by combining it with CCD camera. The proposed method provides high qualit
We propose a novel framework for upconversion of depth video resolution in both spatial and time domains considering spatial and temporal coherences. Although the Time-of-Flight (TOF) sensor which is widely used in computer vision fields provides depth video in realtime, it also provides a low resolution and a low frame-rate depth video. We propose a cheaper solution that enhances depth video obtained from a TOF sensor by combining it with a Charge-coupled Device (CCD) camera in 3D contents whic
Seoul National University Hospital strives to move its hospital information system to a whole new level, which enables customized healthcare service and fulfills individual requirements. The current information strategy is being formulated as an initial step of development, promoting the establishment of next-generation hospital information system.
Recent large-scale genome-wide association studies have identified common genetic variations that may contribute to the risk of amyotrophic lateral sclerosis (ALS). However, pinpointing the risk variants in noncoding regions and underlying biological mechanisms remains a major challenge. Here, we constructed a convolutional neural network model with a large-scale GWAS meta-analysis dataset to unravel functional noncoding variants associated with ALS based on their epigenetic features. After filt
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