Self-Supervised Transformer-Based Pipeline for Liver Tumor Segmentation and Type Classification
The proposed self-supervised, end-to-end pipeline improves liver tumor segmentation and provides accurate tumor type classification, supporting reliable radiologic assessment, treatment planning, and improved prognostication for patients with liver cancer.
Authors
- Mohammad Hamghalam (ORCID: https://orcid.org/0000-0003-2543-0712)
- Jacob Peoples (ORCID: https://orcid.org/0000-0003-0191-7446)
- Ramtin Mojtahedi
- Richard ev k
- Amber L. Simpson
- William R. Jarnagin
Institutions
- Memorial Sloan Kettering Cancer Center (US)
- Qazvin Islamic Azad University (IR)
- Queen's University (CA)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2026-01-30
- DOI
- https://doi.org/10.1200/cci-25-00135
- Primary Topic
- Cholangiocarcinoma and Gallbladder Cancer Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00