K
B-enhanced Pathology CLIP addresses the variability in performance of pathology foundation models across different branches of pathology. By leveraging expert-curated knowledge graphs, the project extracts concepts and their relationships, enriching the training datasets of pathology foundation models. Through manipulating the distribution of concepts in the data, the project aims to develop a model that excels in various pathological domains and subfields. The project utilizes public datasets and expert-curated knowledge graphs to extract important concepts and their relationships. These concepts are then applied into the training datasets, ensuring a balanced and comprehensive representation of various pathological domains. It employs advanced natural language processing (NLP) and computer vision techniques to align the extracted knowledge with the existing data, and fine-tunes the foundation model to optimize its performance across different pathological subfields.
