Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study
Leonard Sunwoo(Seoul National University Bundang Hospital), Jae Hyoung Kim(Seoul National University), Kyong Joon Lee(Seoul National University Bundang Hospital), Yun‐Jung Bae(Korea National University of Transportation), Ji Hee Kang(Seoul National University Hospital), Kwang Gi Kim(Gachon University), Yeonah Kang(Seoul National University), Chul‐Ho Sohn(Seoul National University Hospital), Seung-Hyun Lee(Kwangwoon University), Young Jae Kim(Gachon University), Jihang Kim(Seoul National University), Cheolkyu Jung(Seoul National University), Roh‐Eul Yoo(Seoul National University Hospital), Seung Hong Choi(Seoul National University Hospital), Byung Se Choi(Seoul National University Bundang Hospital)
Cited by 57
Related Papers
2021 Korean Thyroid Imaging Reporting and Data System and Imaging-Based Management of Thyroid Nodules: Korean Society of Thyroid Radiology Consensus Statement and Recommendations
|Korean Journal of Radiology|2021|277
Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients
|Neuro-Oncology|2018|225
Deep Learning in Diagnosis of Maxillary Sinusitis Using Conventional Radiography
|Investigative Radiology|2018|108
Contrast-enhanced MRI T1 Mapping for Quantitative Evaluation of Putative Dynamic Glymphatic Activity in the Human Brain in Sleep-Wake States
|Radiology|2021|94
Radiomics prognostication model in glioblastoma using diffusion- and perfusion-weighted MRI
|Scientific Reports|2020|77