A transparent, lightweight and sustainable Green Learning AI model for prostate cancer detection on <scp>MRI</scp>
A novel GL model fully automatically detects csPCa on prostate biparametric MRI with comparable performance to PI-RADS and DL. Combined with PI-RADS, GL significantly improves csPCa detection.
Authors
- Vasileios Magoulianitis (ORCID: https://orcid.org/0009-0005-3907-1003)
- Karanvir Gill (ORCID: https://orcid.org/0000-0003-3437-269X)
- André L. T. Abreu
- Jintang Xue (ORCID: https://orcid.org/0009-0004-3531-8147)
- Suzanne L. Palmer (ORCID: https://orcid.org/0000-0002-3492-7459)
- C. Jay Kuo
- Masatomo Kaneko (ORCID: https://orcid.org/0000-0002-1205-807X)
- V. Duddalwar
- Jinyuan Liu (ORCID: https://orcid.org/0000-0001-6689-8245)
- Jiaxin Yang (ORCID: https://orcid.org/0000-0001-9286-0558)
- C.L. Nikias
- Lorenzo Storino Ramacciotti
- Manju Aron
- Inderbir S Gill
- Jie Cai
- Giovanni E Cacciamani
- Passant Mohamed
Institutions
- University of Southern California (US)
- Viterbo University (US)
- Prostate Cancer Foundation (US)
- Los Angeles Medical Center (US)
Publication Details
- Journal
- British Journal of Urology
- Published
- 2026-02-26
- DOI
- https://doi.org/10.1111/bju.70203
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00