Yonsei University · Medicine
Professor Sung-Won Kim's research lab specializes in medical image analysis and artificial intelligence, with a focus on improving diagnostic accuracy and prognostic prediction in oncology and gastrointestinal diseases. The lab develops advanced radiomics and deep learning models using MRI, ultrasound, and radiographic imaging to support early detection, differential diagnosis, and personalized treatment planning for cancers such as hepatocellular carcinoma, thyroid cancer, and colorectal disorders. A key emphasis is placed on integrating clinical and imaging biomarkers to enhance disease-free survival prediction and surgical decision-making.
Figures are computed from collected data and may differ slightly.
The prognostic value of the preoperative radiomic model with 3-mm border extension showed comparable performance with that of the postoperative clinicopathologic model for predicting DFS of early recurrence of HCC using gadoxetic acid-enhanced MRI. This suggests the importance of including peritumoral changes in the radiomic analysis of HCC.
It is suggested that risk factors of ipsilateral and contralateral CLN metastases should be considered while planning the extent of CLND in patients with clinically node-negative and unilateral PTC upon preoperative ultrasonography.
This study found that a deep-learning model can discriminate between colonoscopy images of intestinal BD, CD, and ITB. In particular, the algorithm demonstrated superior discrimination ability for typical images. This approach presents a beneficial method for the differential diagnosis of the diseases.
Radiomic features hold potential to improve prediction of disease-free survival (DFS) in triple-negative breast cancer (TNBC) and may show better performance if developed from TNBC patients. We aimed to develop a radiomics score based on MRI features to estimate DFS in patients with TNBC. A total of 228 TNBC patients who underwent preoperative MRI and surgery between April 2012 and December 2016 were included. Patients were temporally divided into the training (n = 169) and validation (n = 59) s
Mobility is the most important component in mobile ad hoc networks (MANETs) and delay-tolerant networks (DTNs). In this paper, we first investigate numerous GPS mobility traces of human mobile nodes and observe superdiffusive behavior in all GPS traces, which is characterized by a ¿faster-than-linear¿ growth rate of the mean square displacement (MSD) of a mobile node. We then investigate a large amount of access point (AP) based traces, and develop a theoretical framework built upon continuous t
The purpose of this study was to develop and test the performance of a deep learning-based algorithm to detect ileocolic intussusception using abdominal radiographs of young children. For the training set, children (≤5 years old) who underwent abdominal radiograph and ultrasonography (US) for suspicion of intussusception from March 2005 to December 2017 were retrospectively included and divided into control and intussusception groups according to the US results. A YOLOv3-based algorithm was deve
We propose Guided-TTS 2, a diffusion-based generative model for high-quality adaptive TTS using untranscribed data. Guided-TTS 2 combines a speaker-conditional diffusion model with a speaker-dependent phoneme classifier for adaptive text-to-speech. We train the speaker-conditional diffusion model on large-scale untranscribed datasets for a classifier-free guidance method and further fine-tune the diffusion model on the reference speech of the target speaker for adaptation, which only takes 40 se
This paper presents a simulation method for the warpage that take places after the patterning process of printed circuit boards. To conduct an efficient as well as realistic simulation, a nonlinear thermo-elasticity problem with cure kinetics is approximated to a linear one by adjusting the thermal loading condition, which is the processing temperature where the stress-free state is assumed. This paper proposes a method that can determine such temperature based on a simple experiment. Moreover,
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