Transfer Learning Strategies for Pathological Foundation Models: A Systematic Evaluation in Brain Tumor Classification
Journal:
arXiv
Published Date:
Jan 19, 2025
Abstract
Foundation models pretrained on large-scale pathology datasets have shown
promising results across various diagnostic tasks. Here, we present a
systematic evaluation of transfer learning strategies for brain tumor
classification using these models. We analyzed 254 cases comprising five major
tumor types: glioblastoma, astrocytoma, oligodendroglioma, primary central
nervous system lymphoma, and metastatic tumors. Comparing state-of-the-art
foundation models with conventional approaches, we found that foundation models
demonstrated robust classification performance with as few as 10 patches per
case, despite the traditional assumption that extensive per-case image sampling
is necessary. Furthermore, our evaluation revealed that simple transfer
learning strategies like linear probing were sufficient, while fine-tuning
often degraded model performance. These findings suggest a paradigm shift from
"training encoders on extensive pathological data" to "querying pre-trained
encoders with labeled datasets", providing practical implications for
implementing AI-assisted diagnosis in clinical pathology.
