Fully automated segmentation and classification of renal tumors on CT scans via machine learning
Our automatic segmentation and classifier model showed promising results for renal tumor segmentation and classification.
Authors
- Min Ho Kang (ORCID: https://orcid.org/0000-0003-1366-8942)
- Taek Min Kim (ORCID: https://orcid.org/0000-0001-6915-8795)
- Sang Youn Kim (ORCID: https://orcid.org/0000-0003-4038-7345)
- Ja Hyeon Ku (ORCID: https://orcid.org/0000-0002-0391-2342)
- J.Y. Peter Ko
- Jang Hee Han (ORCID: https://orcid.org/0000-0003-4942-4632)
- Chang Wook Jeong (ORCID: https://orcid.org/0000-0002-2200-5019)
- Cheol Kwak (ORCID: https://orcid.org/0000-0002-1987-2111)
- Seung Jae Choi (ORCID: https://orcid.org/0009-0003-9331-7209)
- Jeong Yeon Cho (ORCID: https://orcid.org/0000-0001-8659-0918)
- Byung Woo Kim
- Young-Gon Kim
Institutions
- New Generation University College (ET)
- Seoul National University Hospital (KR)
- National University College (PR)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-01-29
- DOI
- https://doi.org/10.1186/s12885-025-13582-6
- Citations
- 12
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 16.32
Funders
- Seoul National University
- Korea Health Industry Development Institute
- College of Medicine, Seoul National University