Abstract
The detection of oral squamous cell carcinoma (OSCC) in histopathology images is crucial for improving diagnostic accuracy and patient outcomes. Here, we present a protocol for detecting OSCC in histopathology images using transfer learning. We describe steps for installing software and prerequisites, preparing datasets, and pretraining a model on images from various tissue types using the momentum contrast (MoCo) framework. We then detail procedures for evaluating the fine-tuned HistoMOCO model's performance on a test dataset.