Education:
Doohee Lee holds a B.S. and M.S. in Computer Science Engineering and is currently a Ph.D. candidate at Kangwon National University. His academic background has equipped him with a strong foundation in deep learning, medical image analysis, and AI applications in healthcare. 🎓📚
Experience:
With extensive experience in both academia and industry, Doohee Lee has contributed significantly to the field of medical AI. As the COO of ZIOVISION, he leads R&D teams in the development of cutting-edge medical imaging technologies. His previous roles at MEDICALIP Co., Ltd. and Seoul National University Hospital have allowed him to advance research projects and industry collaborations in the AI healthcare space. 💼🧑💻
Awards and Honors:
Doohee Lee’s groundbreaking work has earned him numerous accolades, including recognition in AI-driven medical imaging advancements. His efforts have led to significant developments in the field of medical diagnostics, especially in AI-based image segmentation and automated analysis. 🏆👏
Research Focus:
Doohee Lee specializes in AI-driven medical image analysis, focusing on deep learning-based segmentation, 3D image analysis, and clinical AI applications. His ongoing research includes automated tumor segmentation, sepsis mortality prediction, and osteoporosis grading via CT. He has also worked on developing AI models for predictive healthcare solutions. 🧠💻
Conclusion:
Doohee Lee’s expertise in medical AI and his leadership at ZIOVISION continue to drive innovation in healthcare. With a strong focus on utilizing AI to improve diagnostic accuracy and patient outcomes, he is at the forefront of technological advancements in the medical imaging sector. His contributions are shaping the future of AI-powered healthcare solutions. 🌐💪
Publications:
A Refined Approach to Segmenting and Quantifying Inter-Fracture Spaces in Facial Bone CT Imaging (2025) – Applied Sciences
DOI: 10.3390/app15031539
Cited by: 10 citations 📑
Very fast, high-resolution aggregation 3D detection CAM to quickly and accurately find facial fracture areas (2024) – Computer Methods and Programs in Biomedicine
DOI: 10.1016/j.cmpb.2024.108379
Cited by: 5 citations 📑
Deep Learning-Based Dual-Stage Model for Accurate Nasogastric Tube Positioning in Chest Radiographs (2024) – SSRN
DOI: 10.2139/ssrn.4965848
Cited by: 3 citations 📑
Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study (2024) – Journal of Korean Medical Science
DOI: 10.3346/jkms.2024.39.e53
Cited by: 15 citations 📑
Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning (2022) – Journal of Magnetic Resonance Imaging
DOI: 10.1002/jmri.28332
Cited by: 50 citations 📑
Development of a deep learning-based auto-segmentation algorithm for hepatocellular carcinoma (HCC) and application to predict microvascular invasion of HCC using CT texture analysis: preliminary results. (2022) – Acta radiologica (Stockholm, Sweden: 1987)
DOI: 10.1177/02841851221100318
Cited by: 20 citations 📑
Clinical application of patient-specific 3D printing brain tumor model production system for neurosurgery (2021) – Scientific Reports
DOI: 10.1038/s41598-021-86546-y
Cited by: 30 citations 📑