Machine Learning Models Using General and Tissue-Specific Feature Extractors for Accurate Subtyping of Biopsy Samples: Advancing Lung Cancer Diagnosis in Latin America
These findings underscore the effectiveness of task-specific and general feature extractors in delivering accurate, explainable results and address a gap in artificial intelligence-driven histopathology advancements, paving the way for future clinical applications.
Authors
- Gabriel da Silva (ORCID: https://orcid.org/0000-0003-4284-4474)
- Vladmir Cláudio Cordeiro de Lima (ORCID: https://orcid.org/0000-0003-3955-1101)
- Guilherme Velozo (ORCID: https://orcid.org/0009-0001-8959-1526)
- Viviane Teixeira Loiola de Alencar (ORCID: https://orcid.org/0000-0003-2689-376X)
- Felipe Emerson André Alves (ORCID: https://orcid.org/0000-0002-7217-8639)
- Iusta Caminha (ORCID: https://orcid.org/0000-0002-6932-6522)
- Fábio Távora (ORCID: https://orcid.org/0000-0003-2361-0613)
- Luiz Edmundo Lopes Mizutani
Institutions
- Universidade Federal do Ceará (BR)
- AC Camargo Hospital (BR)
- Universidade de Fortaleza (BR)
- Centro de Pesquisas Oncológicas (BR)
Publication Details
- Journal
- JTO Clinical and Research Reports
- Published
- 2025-09-23
- DOI
- https://doi.org/10.1016/j.jtocrr.2025.100906
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00