Yonsei University · Medicine
Professor Hwiyoung Kim's research lab specializes in the application of artificial intelligence and machine learning to medical image analysis, with a focus on improving diagnostic accuracy and clinical decision-making in radiology and orthopedics. The lab develops explainable AI models and deep learning frameworks—such as multi-scale U-Net architectures—for automated segmentation and detection of anatomical structures, including organoids and pulmonary nodules. A key emphasis is on enhancing reproducibility and reliability in medical imaging, particularly through robust, interpretable, and adversarial-robust AI systems for clinical deployment. The lab also investigates psychosocial predictors in caregiver stress using machine learning, aiming to support early intervention in pediatric mental health.
Figures are computed from collected data and may differ slightly.
Contrary to 2D cells, 3D organoid structures are composed of diverse cell types and exhibit morphologies of various sizes. Although researchers frequently monitor morphological changes, analyzing every structure with the naked eye is difficult. Given that deep learning (DL) has been used for 2D cell image segmentation, a trained DL model may assist researchers in organoid image recognition and analysis. In this study, we developed OrgaExtractor, an easy-to-use DL model based on multi-scale U-Net
Due to rapid developments in the deep learning model, artificial intelligence (AI) models are expected to enhance clinical diagnostic ability and work efficiency by assisting physicians. Therefore, many hospitals and private companies are competing to develop AI-based automatic diagnostic systems using medical images. In the near future, many deep learning-based automatic diagnostic systems would be used clinically. However, the possibility of adversarial attacks exploiting certain vulnerabiliti
This decision analytical modeling study found that the DLBS model was more sensitive to detecting pulmonary nodules on chest radiographs compared with the original model. These findings suggest that the DLBS model could be beneficial to radiologists in the detection of lung nodules in chest radiographs without need of the specialized equipment or increase of radiation dose.
By using explainable machine learning models (XGBoost and RF), we investigated major predictors for each subscale of the parenting stress index in caregivers of ASD patients. Identified predictors for parenting stress in this population might help alert clinicians whether a caregiver is at a high risk of experiencing severe parenting stress and if so, providing timely interventions, which could eventually improve the treatment outcome for ASD patients.
Category: Ankle, Ankle Arthritis Introduction/Purpose: The Takakura staging system has been used for the stratification in ankle osteoarthritis(OA). Patient’s OA stage is determined by visual examination on the status of talar and distal tibia in anteroposterior ankle radiograph. Clinical decisions about whether to treat conservatively or to treat with operation such as supra-malleolar osteotomy or arthroplasty may depend on this grading system. However, this is not completely reproducible betwe
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