박상현
Park, Sang HyunDepartment of Robotics and Mechatronics Engineering
학력
- 2008 ~ 2014서울대학교 박사
- 2010 ~ 2010서울대학교 석사
- 2004 ~ 2008연세대학교 학사
경력
- 2017 ~ Current대구경북과학기술원 / 조교수
- 2016 ~ 2017SRI International
- 2014 ~ 2016University of Norty Carolina
수상실적
- 2023 총장표창 / DGIST
- 2020 공로상 / DGIST
연구실 소개
- Medical Image & Signal Processing Lab
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We are interested in the development of advanced artificial intelligence (AI) algorithms for medical image analysis and robot intelligence. The following studies are being conducted.
- Computer vision / Machine learning / Deep learning- Medical image analysis (classification, enhancement, segmentation, registration)- Pathology image analysis- Robot tracking- Bio signal processing
Related Keyword
- "Subject-adaptive meta-learning for personalized BCI: A fusion of resting-state EEG signal and task-specific information", Information Fusion, v.125
- "MOSInversion: Knowledge distillation-based incremental learning in organ segmentation using DeepInversion", Computers in Biology and Medicine, v.199
- "해부학적 제약과 교차 어텐션을 통한 CT 및 디지털 단층촬영술의 자가 지도 비강체 정합", 제어.로봇.시스템학회 논문지, v.31, no.11, pp.1240 - 1247
- "Efficient One-shot Federated Learning on Medical Data using Knowledge Distillation with Image Synthesis and Client Model Adaptation", Kang, Myeongkyun. (2025-10). Efficient One-shot Federated Learning on Medical Data using Knowledge Distillation with Image Synthesis and Client Model Adaptation. Medical Image Analysis, 105. doi: 10.1016/j.media.2025.103714
- "Lumbar Spinal Stenosis Grading in Multiple Level Magnetic Resonance Imaging Using Deep Convolutional Neural Networks", Won, Dongkyu. (2025-05). Lumbar Spinal Stenosis Grading in Multiple Level Magnetic Resonance Imaging Using Deep Convolutional Neural Networks. Global Spine Journal, 15(4), 2309–2317. doi: 10.1177/21925682241299332
- "Revisiting Masked Image Modeling with Standardized Color Space for Domain Generalized Fundus Photography Classification", International Conference on Medical Image Computing and Computer Assisted Interventions, pp.538 - 548
- "MC-NuSeg: Multi-Contour Aware Nuclei Instance Segmentation with Segment Anything Model", Information Processing in Medical Imaging, IPMI 2025, pp.283 - 296
- "Self-supervised Deformable Registration of Digital Tomosynthesis and 3D CT Images for Surgical Navigation", Park, Muyul. (2025-02-19). Self-supervised Deformable Registration of Digital Tomosynthesis and 3D CT Images for Surgical Navigation. SPIE 2025 Conference on Medical Imaging: Image Processing, 134061D-1-134061D–6. doi: 10.1117/12.3046301
- "Low-Shot Prompt Tuning for Multiple Instance Learning Based Histology Classification", Chikontwe, Philip. (2024-10-07). Low-Shot Prompt Tuning for Multiple Instance Learning Based Histology Classification. International Conference on Medical Image Computing and Computer Assisted Interventions, 285–295. doi: 10.1007/978-3-031-72083-3_27
- "InstaSAM: Instance-Aware Segment Any Nuclei Model with Point Annotations", Nam, Siwoo. (2024-10-07). InstaSAM: Instance-Aware Segment Any Nuclei Model with Point Annotations. International Conference on Medical Image Computing and Computer Assisted Interventions, 232–242. doi: 10.1007/978-3-031-72083-3_22
연구 뉴스
- Beginning of Energy Innovation! DGIST Successfully Develops Next-gen Semi-permanent Battery Technology 2024-05-18
- DGIST-Stanford Joint Research Team Successfully Developed Novel Medical AI Model based on Federated Learning! Expected to Take the First Step in the Era of Large-scale AI 2024-05-18
- DGIST-스탠퍼드 공동연구팀, 연합학습 기반 새로운 의료 AI 모델 개발! 대규모 의료인공지능 시대 첫걸음 기대 2024-05-03
- Prof. Sang Hyun Park's research team has collaborated with a Stanford University research team to enhance defect detection performance in smart factories! 2024-03-23
- 박상현 교수팀, 스탠퍼드 대학 연구팀과 협력해 스마트 팩토리의 불량 검출 성능 크게 향상시켰다! 2024-03-14
연구분야
미래유망 신기술(6T)
국가과학기술표준분류