AI in Medical Imaging & Healthcare (AIMHI Lab) — We Aim High
AIMHI Lab at Gachon University, led by Prof. Kwang-Hyun Uhm, advances medical AI from CT/MRI and pathology foundation models to surgical planning, segmentation, and multimodal learning. We participate in MICCAI challenges and develop AI for diagnosis, prognosis, and precision medicine.
가천대학교 인공지능학과 엄광현 교수 연구실 (AIMHI Lab)은 의료영상 AI(CT/MRI), 병리 AI, 수술 계획 AI 및 개인맞춤 의료 인공지능 연구를 수행합니다.
Pioneering AI research for medical imaging and healthcare — from CT and MRI to pathology slides. Our mission: advancing diagnosis, surgical support, and personalized treatment through cutting-edge AI.
Our research covers segmentation, disease classification, image generation/restoration, prognosis prediction, and 3D visualization.
By integrating deep learning with medicine and engineering, we aim to improve early diagnosis, surgical assistance, and personalized treatment.
AI beyond theory: How artificial intelligence is transforming industries through real-world applications in healthcare, imaging, and data science.
AI × Medicine: Development of AI-driven systems for disease diagnosis, prognosis prediction, and surgical planning assistance.
Practical learning: Emphasis on hands-on projects, interdisciplinary collaboration, and real-world problem solving in AI education.
As CTO of MEDAI, I lead the development of next-generation AI solutions that support clinicians throughout the entire care pathway.
Comprehensive Support: From diagnosis to surgery, our AI assists medical professionals in decision-making and treatment planning
3D Surgical Innovation: Enhancing the safety and precision of surgical procedures.
Transforming Healthcare: Our mission is to empower clinicians to deliver faster, more accurate, and truly personalized patient care.
Oral presentation by Kwang-Hyun Uhm (AIMHI Lab) at the MICCAI 2025 UNICORN Challenge, held on September 23, 2025, in Daejeon, Korea. Our team achieved 2nd Place in this challenge.
Oral presentation by Inhwa Son (AIMHI Lab) at the MICCAI 2025 AIMS-TBI Challenge, held on September 23, 2025, in Daejeon, Korea. Our team achieved 2nd Place in this challenge.
📄 2025 Publication in IEEE Transactions on Medical Imaging (TMI)
“An Anisotropic Cross-View Texture Transfer with Multi-Reference Non-Local Attention for CT Slice Interpolation”
Published in IEEE TMI — the flagship and most prestigious journal in medical imaging.
Impact Factor (2025): 9.8
Acceptance Rate: ~12.5%
JCR: Top 2.1% (Q1) in Radiology, Nuclear Medicine & Medical Imaging
👉 [Read the paper]
📄 2024 Publication in Computers in Biology and Medicine (CBM)
“Lesion-aware Cross-Phase Attention Network for Renal Tumor Subtype Classification on Multi-Phase CT Scans”
Published in CBM — a well-recognized interdisciplinary journal bridging computer science and medicine.
Impact Factor (2025): 6.3
JCR: Top 5.2% (Q1) in Mathematical & Computational Biology
👉 [Read the paper]
