ScholarWorks@Gachon: Kim, Young Jae

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Kim, Young Jae (김영재)

IT (의공학과)

Research Area: 의료 인공지능, 의료 영상처리, 컴퓨터 보조 진단, Medical AI, Medical Image Processing, Computer Aided Diagnosis

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Deep learning:20||Deep Learning:15||Segmentation:12||Artificial Intelligence:11||deep learning:9||Radiomics:8||Machine learning:8||딥러닝:6||Classification:6||artificial intelligence:6||Computed tomography:5||Artificial intelligence:5||machine learning:4||Radiography:4||Sarcopenia:4||UNet:4||ResNet50:3||prognosis:3||Computeraided diagnosis:3||RetinaNet:3||인공지능:3||Gastroscopy:3||ResNet:3||Machine Learning:2||Gastric cancer:2||Spine:2||intracameral illumination:2||Quantitative:2||Computerassisted diagnosis:2||Accuracy:2||Convolutional Neural Network:2||Tomography:2||chest Xray:2||내시경:2||Cervical cancer:2||Laryngoscopy:2||convolutional neural network:2||분할:2||cornea:2||CNN:2||Ultrasonography:2||Diagnostic imaging:2||endoscopy:2||Distal radius fractures:2||Inception:2||COVID19:2||Artificial Intelligent:2||Data Collection:1||Data augmentation:1||Dataset:1||Deep Neural Network:1||Deep vein thrombosis:1||DenseNet:1||Dental Digital radiography:1||Dental Implant:1||Diagnosis:1||Diagnostic Imaging:1||Digital Image Processing:1||EGFR mutation:1||Elbow pain:1||Embolisation:1||Embolization:1||Endoscope:1||Endoscopic Image:1||Endoscopic ultrasonography:1||Endoscopy:1||Endosonography:1||Erythema:1||Euclidean distance:1||Eye Detection:1||Feature selection:1||Female:1||Fertility:1||Follicular neoplasm:1||Fractal Dimension:1||Gallbladder disease:1||Gastric Ulcer:1||Gastric ulcer:1||GastroBaseNet:1||Gastrointestinal malignancy:1||Gastroscope:1||Gastroscopy image:1||Generative adversarial network:1||Genital Neoplasms:1||Greulich and Pyle atlas:1||Hand Sanitizers:1||Hand bone:1||Health Information Systems:1||Hepatic resection:1||Hepatic transplantation:1||High Resolution CT(HRCT):1||Histogram Equalization:1||Histogram equalization:1||Hologram:1||Honeycomb:1||Hospital:1||Hurst Exponent:1||Hypervascular staining:1||IBD:1||IMT:1||Identification:1||Image Classification Model:1||Image Processing:1||Image quality assessment algorithm:1||Image segmentation:1||ImageNet:1||Imaging:1||Imprinted text:1||InceptionResNet:1||Inflammatory bowel disease:1||Influenza vaccination:1||Informatics:1||Infrared Image:1||Infrared Rays:1||Internet:1||Internet of Things:1||Keras OCR:1||Knee pain:1||Laparoscope:1||Laplacian variance:1||Laryngeal disease:1||Lesion detection:1||Line Profile:1||Liver Segmentation:1||Liver cancer:1||Localization:1||Lumbar lordosis:1||Lung Cancer:1||Lung adenocarcinoma:1||Lung cancer:1||Lymphedema:1||Mammography:1||Mammograpy:1||Mask RCNN:1||Medial epicondylitis:1||Medical Informatics:1||Medical Training:1||Minimally Invasive Surgical Procedures:1||Morphological Analysis:1||Motion detection:1||Motor Skills:1||Myoma:1||Myomectomy:1||Nasal fractures:1||Navigation MRI:1||Neoplasm metastasis:1||Neural network:1||Nonsmall cell lung cancer (NSCLC):1||Nurse–patient ratio:1||Nurse staffing:1||Nurse staffing grades:1||Nurse staffing level:1||Nursing shortage:1||Object Detection:1||Object detection:1||Optical flow:1||Oral Surgical Procedures:1||Osseointegration:1||Osteoarthritis:1||Pancreas:1||Pancreatic cyst lesion:1||Panoramic radiography:1||Papillary:1||Parathyroid gland:1||Pectoralis muscles:1||Pelvic fractures:1||Pill Classification:1||Pill identification:1||Polyps:1||Posterior fossa:1||Preprocessing:1||Pretrained UNet:1||Product Labeling:1||Prognostic factor:1||Prone MRI:1||Prone position:1||Prostate cancer:1||Quantitative Evaluation:1||Quantitative biomarker:1||Quantitative measurement:1||RGB channel superposition:1||Radiodensity:1||Radiomic feature:1||Rectal cancer:1||Recurrence:1||Resnet:1||Response Assessment in NeuroOncology Brain Metastases (RANOBM):1||Response evaluation criteria in solid tumors:1||Scoliosis:1||Segmentation. Convolution Neural Network:1||Shoulder disease:1||Skeletal muscle:1||Skin prick test:1||Skull fractures:1||Small computer:1||Social Media:1||Software:1||Spinal metastases:1||Support Vector Machine:1||Support vector machine:1||Surgery:1||Surgical technique:1||Tanner and Whitehouse atlas:1||Template Matching:1||Thoracic kyphosis:1||Thorax:1||Threedimension:1||Thyroid Cancer:1||Transfer learning:1||Traumatic brain injury:1||Tumor:1||Tumor volume:1||UC:1||Ulcer:1||Upper Endoscopy:1||Urinary tract infection:1||VGGnet:1||Vertebral compression fracture:1||Vertebral compression ratio:1||VggNet:1||Virtual Surgery Simulation:1||Vocal Cord:1||Volumetric liver:1||XRay Computed:1||Xray:1||Xray computed:1||Xrays:1||XGBoost:1||YOLO:1||acetowhite:1||algorithms:1||anaplastic lymphoma kinase:1||benign paroxysmal positional vertigo:1||bone graft substitute:1||brain metastasis:1||cancer cachexia:1||cataract surgery:1||cervical cancer:1||chemoembolization:1||chemotherapy:1||chest radiograph:1||chronic sinusitis:1||colon cancer:1||computeraided detection:1||conjunctiva:1||contrastenhanced CT:1||data normalization:1||detection:1||detection lesion of mass:1||diagnosis:1||diagnostic performance:1||economic characteristics:1||endoscopic score:1||epidermal growth factor receptor:1||gastric lesion:1||gastroscopy:1||gradient boosting classifier:1||hepatocellular carcinoma:1||horizontal nystagmus:1||illuminance:1||image analysis:1||image processing:1||imaging:1||irradiance:1||light exposure:1||logistic regression:1||lung cancer:1||machinelearning:1||microscope:1||motion analysis:1||multiview network:1||multiclass segmentation:1||neovascularisation:1||nystagmus:1||nystagmus detection:1||object detection:1||obstructive sleep apnea:1||otitis media with effusion:1||otoendoscope:1||overall survival:1||paranasal sinus view:1||paranasal sinuses:1||pediatric:1||phototoxicity:1||pneumonia:1||posterior lumbar interbody fusion:1||predict model:1||properties by areas:1||prospective study:1||pupil:1||pupil tracking:1||radiography:1||radiomic feature:1||radiomics:1||random forest:1||rehospitalization:1||ribs:1||sarcopenia:1||segmentation:1||sinusitis:1||spinal stenosis:1||stromal vascular fraction:1||support vector machine:1||survival analysis:1||texture analysis:1||thorax:1||threedimensional region growing:1||transfer learning:1||transitional zone prostate cancer:1||treatment response:1||tympanic membrane:1||ulcerative colitis:1||객체 검출:1||검출:1||고해상도 컴퓨터 단층촬영:1||광학 흐름:1||궤양성 대장염:1||낭포:1||내시경 영상:1||내시경 점수 시스템:1||대장 내시경:1||대장암:1||동공:1||레티나넷:1||봉와양폐:1||안진:1||알고리즘:1||어깨 질환:1||염증성 장질환:1||욜로:1||움직임 감지:1||움직임 분석:1||위 병변:1||위궤양:1||위내시경 영상:1||인공지능 모델:1||자동 검출:1||척추측만증:1||컴퓨터 보조 진단:1||콥 각도:1||합성곱 신경망:1||.:1||형태학적 분석:1||3D visualization:1||3Dclassification:1||AI:1||AI model:1||Abnormal neovessels:1||Absorptiometry, photon:1||Adenocarcinoma:1||Adoption:1||AlexNet:1||Algorithms:1||Artificial neural network:1||Attention UNet:1||Augmented Reality:1||Automatism:1||Biomarker:1||Blink Detection:1||Body Mass Index (BMI):1||Body composition:1||Bone age:1||Brain Image Segmentation:1||Brain Tumor:1||Breast Mass:1||Breast mammogram:1||CD:1||Cancer:1||Cardiovascular disease:1||Cataract surgery:1||Cervix Cancer:1||Chemotherapy:1||Cobb angle:1||Cohort studies:1||Colorectal Endoscope:1||Colorectal neoplasms:1||Computed Tomography:1||Computed Tomography (CT):1||Computed aided diagnosis (CAD):1||Computer Aided Diagnosis(CAD):1||Computer vision:1||ComputerAided Diagnosis:1||Computerassisted radiographic image interpretation:1||Convolutional Neual Network:1||Convolutional Neural Network (CNN):1||Cracked tooth syndrome:1||Custom Loss Function:1||Cyst:1||DEXA:1||

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