Korea Advanced Institute of Science and Technology · Computer Science
Professor Hong Joo Lee's research lab specializes in advanced computational methods and materials science with a focus on medical image analysis, deep learning for biomedical applications, and functional micro/nanofabrication. The lab develops innovative AI-driven segmentation and facial landmark detection techniques that integrate geometric priors and uncertainty estimation to enhance accuracy and robustness in complex medical and visual data. Additionally, the lab explores the fabrication of microfluidic devices using ceramic-polymer materials for applications in lab-on-a-chip systems and MEMS, emphasizing thermal and mechanical stability. The research bridges artificial intelligence, biomedical engineering, and advanced materials for next-generation diagnostic and sensing technologies.
Figures are computed from collected data and may differ slightly.
In this paper, we propose a novel image segmentation method to tackle two critical problems of medical image, which are (i) ambiguity of structure boundary in the medical image domain and (ii) uncertainty of the segmented region without specialized domain knowledge. To solve those two problems in automatic medical segmentation, we propose a novel structure boundary preserving segmentation framework. To this end, the boundary key point selection algorithm is proposed. In the proposed algorithm, t
Two-dimensional simulations of photon acceleration by using a laser wake field are presented with a fully electromagnetic and relativistic particle-in-cell code. The frequency increase of about 10% is observed, which is saturated mainly by diffraction and dispersion of the laser pulse. Images of electron density and laser field profiles are presented.
Facial landmark detection plays an important role in face analysis tasks. Moreover, it is used as a prerequisite in many facial related applications, the simplicity, as well as effectiveness, is essential in the facial landmark detection. In this paper, we propose an effective facial landmark detection network and an associated learning framework with the geometric prior-generative adversarial network. The geometric prior-generative adversarial network consists of one generator and two discrimin
Interests on the fabrication of microfluidic devices have increased in the fields of micro total analysis system (μ-TAS) and MEMS (Microelectromechanical systems) due to their chemical inertness and high thermal stability. The thermal characterization of the SiCN preceramic polymer, polyvinylsilazane, showed that the cured polymer has ceramic properties at heat treatment temperature of 600 oC or above. In the characterization of the mechanical properties, the characteristic values of the elastic
Open papers in the app to read, cite, and organize with AI.
