Academics | The Hong Kong University of Science and Technology

Data filtering can filter out data with high noise level

This project is focused on constructing a comprehensive benchmark to evaluate the performance of multimodal large language models (MLLMs) in breast cancer tasks.

The "Scaling Law for Pathology" project is aimed at exploring the efficacy of scaling laws within the specialized domain of pathology.

The high cost of labeling in the medical field has led to a lack of annotated data related to whole slide imaging (WSI), thereby limiting the performance of many downstream tasks, such as training and application of pathology CLIP.

KB-enhanced Pathology CLIP addresses the variability in performance of pathology foundation models across different branches of pathology.

Our research is focused on constructing a comprehensive benchmark to evaluate the performance

Acquiring high-quality training data is critical for the development of highly accurate and robust machine learning models, particularly as foundational models emerge.

Mining and analyzing private pathology data, and the rich information in the field of pathology is integrated by constructing refined pathology knowledge graph.