HTRecNet: a deep learning study for efficient and accurate diagnosis of hepatocellular carcinoma and cholangiocarcinoma
HTRecNet markedly enhances the capability for early and accurate differentiation of HCC and CCA from normal liver tissues. Its high diagnostic accuracy and efficiency position it as an invaluable tool in clinical settings, potentially transforming liver cancer diagnostic protocols. This system offers substantial support for refining diagnostic workflows in healthcare environments focused on liver malignancies.
Authors
- Jingze Li (ORCID: https://orcid.org/0000-0002-8455-7601)
- Jiang Du (ORCID: https://orcid.org/0000-0003-4014-1070)
- Jiong Mu (ORCID: https://orcid.org/0000-0002-5295-9630)
- Yujie Wang (ORCID: https://orcid.org/0000-0002-7476-0542)
- Jian Wang (ORCID: https://orcid.org/0000-0001-5457-4059)
- Yupeng Niu
- Jiani Wu (ORCID: https://orcid.org/0000-0003-4954-0692)
- Weichen Guo
Institutions
- Sichuan Agricultural University (CN)
- Affiliated Hospital of Southwest Medical University (CN)
Publication Details
- Journal
- Frontiers in Cell and Developmental Biology
- Published
- 2025-03-24
- DOI
- https://doi.org/10.3389/fcell.2025.1549811
- Citations
- 3
- Primary Topic
- Cholangiocarcinoma and Gallbladder Cancer Studies
- Type
- article
- Field-Weighted Citation Impact
- 6.02