Yoon Seong Choi

TL;DR: This paper presented the largest FL study to date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature.

TL;DR: The authors' fully automated hybrid model demonstrated the potential to be a highly reproducible and generalizable tool across different datasets for the noninvasive prediction of the IDH status of gliomas.

TL;DR: Evaluating the role of radiomics features of postcontrast T1-weighted images, apparent diffusion coefficient, and fractional anisotropy maps in the differentiation of grades and histological subtypes of meningiomas found that feature-based machine learning classifiers of T1C images, ADC, and FA maps are useful for differentiating menioma grades.

TL;DR: In this paper, a novel cardiovascular risk stratification system based on deep-learning-predicted Coronary artery calcium (CAC) score from retinal photographs was developed and validated.