A flexible deep learning framework for liver tumor diagnosis using variable multi-phase contrast-enhanced CT scans
The proposed H-LSTM model can be employed for classification tasks involving incomplete phases of CECT images in real-world scenarios, demonstrating high performance. This highlights the potential of AI-assisted systems in achieving accurate diagnosis and treatment of liver cancer. H-LSTM offers an effective solution for processing multi-phase data and provides practical value for clinical diagnostics.
Authors
- Jiawei Luo (ORCID: https://orcid.org/0000-0003-1617-3224)
- Kexue Pu (ORCID: https://orcid.org/0000-0002-2339-3316)
- Shixin Huang (ORCID: https://orcid.org/0009-0001-7931-7056)
- Xiaoyu Wan (ORCID: https://orcid.org/0000-0002-5587-9353)
- Xixi Nie (ORCID: https://orcid.org/0000-0002-2182-8363)
Institutions
- Chongqing University of Posts and Telecommunications (CN)
- Sichuan University (CN)
- West China Hospital of Sichuan University (CN)
- First People's Hospital of Chongqing (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Journal of Cancer Research and Clinical Oncology
- Published
- 2024-10-03
- DOI
- https://doi.org/10.1007/s00432-024-05977-y
- Citations
- 8
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 3.23
Funders
- Chongqing University
- Chongqing Medical University
- Chongqing University of Posts and Telecommunications
- National Office for Philosophy and Social Sciences