Faculty Directory
- Name
- Oh, Ji-eun
- Faculty Appointment
(title, department)
-
Assistant Professor,
Department of Cancer Biomedical Science.
Researcher, Healthcare AI Team.
- Area of Expertise
- Medical image processing
- Contact no
- +82-31-920-2250
- E-mail
- jieun12@ncc.re.kr
- Work Experience
-
2021-Present, Assistant Professor, Healthcare AI Team, National Cancer Center
2019-2020 Postdoctoral Researcher, Division of Technology Convergence, Research Institute, National Cancer Center
2013-2018 Researcher, Division of Technology Convergence, Research Institute, National Cancer Center
2011, Researcher, Vatech
- Developed CT reconstruction algorithm to reduce metal artifact and radiation dose.
2009-2010, Researcher, Rayence
- Developed the optimized scrubbing sequence of X-ray detector to correct dynamic offset
- Educational Background
-
PhD, Radiological Science, Yonsei University, 2013
MS, Radiological Science, Yonsei University, 2008
BS, Radiological Science, Yonsei University, 2006
- Research Interests
-
Medical image analysis
Deep learning, Computer vision
Precision medicine
- International Collaboration
-
Member of Institute of Electrical and Electronics Engineers (IEEE)
- Achievements
-
• Moving from 2D to 3D: Volumetric Medical Image Classification for Rectal Cancer Staging, MICCAI conference, 2022
• Automated Pulmonary Function from Preoperative CT Scans with Deep Learning, IEEE BHI-BSN conference, 2022
• Measuring FEV1 from Preoperative Chest Computed Tomography Scans using Deep Learning, RSNA conference, 2022
• Reducing the Model Variance of a Rectal Cancer Segmentation Network , IEEE Access, 2019
• Rectal cancer: Toward fully automatic discrimination of T2 and T3 rectal cancers using deep convolutional neural networ
CInternational Journal of Imaging Systems and Technology, 2019
• Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer CANCER RESEARCH AND TREATMENT, 2019
• Soft-compression Mammography Based on Weighted l1-norm Scatter Correction Scheme for Reducing Patient Pain during
Breast Examination JOURNAL OF THE KOREAN PHYSICAL SOCIETY, 2018
• A new software scheme for scatter correction based on a simple radiographic scattering model Medical Biological Engineering and Computing, 2018
• A Compressed-Sensing Based Blind Deconvolution Method for Image deblurring in dental conebeam CT JOURNAL OF DIGITAL IMAGING, 2018
• A blind-deblurring method based on a compressed-sensing scheme in digital breast tomosynthesis Optics and Lasers in Engineering, 2018
• Clustered Microcalcification Detection in Digital Mammography for Various Breast Densities Journal of medical imaging and health informatics, 2018
• Automatic computer aided analysis of optic disc pallor in fundus photographs Acta Ophthalmologics, 2018
• Tumor Identification in Colorectal Histology Images Using a Convolutional Neural Network Journal of Digital Imaging, 2018
• Application of a dual-resolution voxelization scheme to compressed-sensing (CS)-based iterative reconstruction in digital tomosynthesis (DTS) Nuclear Instruments and Methods in Physics Research Section A , 2018
• Automatic Detection of Pectoral Muscle Region for Computer-Aided Diagnosis Using MIAS Mammograms BioMed Research International, 2016
• Efficacy of automated computer-aided diagnosis of retinal nerve fiber layer defects in healthcare screening British journal of Ophthalmology, 2016
• Automatic Computer-Aided Diagnosis of Retinal Nerve Fiber Layer Defects Using Fundus Photographs in Optic Neuropathy
Investigative ophthalmology visual science, 2015
