Spirochetosis detection in colon histopathology images via fine-tuning and boosting techniques using foundation models

Published: 27 Apr 2024, Last Modified: 27 May 2024MIDL 2024 Short PapersEveryoneRevisionsBibTeXCC BY 4.0
Keywords: Human intestinal spirochetosis, bacteria, histopathology, foundation models, transfer learning, feature extraction, XGBoost
Abstract: Spirochetes are bacteria that can be found on the boundaries of colon epithelial tissue, causing several diseases ranging from spirochetosis, inflammatory bowel disease, to cancer. Despite their relevance, spirochetes often remain undetected in histological analysis. We propose the first computational pathology approach to characterize spirochetes, leveraging prior spatial knowledge to detect spirochetes in whole-slide images of colon polyps and biopsies, and differentiate these bacteria as belonging to normal or abnormal tissue. We focus on transfer learning by fine-tuning state-of-the-art computational pathology foundation models and by training an additional XGBoost classifier on downstream tasks.
Submission Number: 99