Enhancing basal cell carcinoma classification in preoperative biopsies via transfer learning with weakly supervised graph transformers.
Journal:
BMC medical imaging
PMID:
40380086
Abstract
BACKGROUND: Basal cell carcinoma (BCC) is the most common skin cancer, placing a significant burden on healthcare systems globally. Developing high-precision automated diagnostics requires large annotated datasets, which are costly and difficult to obtain. This study aimed to fine-tune a weakly supervised machine learning model to classify BCC in preoperative punch biopsies using transfer learning. By addressing challenges of scalability and variability, this approach seeks to enhance generalizability and diagnostic accuracy.