AI in Medical Imaging & Healthcare Innovation (AIMHI) — We Aim High
AI in Medical Imaging & Healthcare Innovation (AIMHI) — We Aim High
Lab Interview — Laboratory for Medical AI and Computer Vision (AIMHI Lab)
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.
We develop AI methods for medical imaging and healthcare, focusing on CT, MRI, ultrasound, and histopathology data.
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.
We work on a wide spectrum of tasks from patient diagnosis to surgical support and outcome prediction, contributing to real-world clinical impact.
CTO Interview — MEDAI Inc.
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
Precision Diagnosis: We leverage medical imaging to identify diseases accurately and reliably.
3D Surgical Innovation: Advanced modeling and reconstruction enhance 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.
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]