MUFASA
Information utility-aware preprocessing for reliable computational pathology model reasoning
MUFASA is an information utility-aware preprocessing framework designed to improve the reliability of model reasoning in computational pathology. The project focuses on improving whole-slide image data quality before downstream machine learning analysis.
The central goal is to remove or reduce the influence of non-diagnostic and low-utility image regions while preserving diagnostically meaningful tissue patterns for reliable downstream prediction.
Role: Collaborating researcher
Key contributions:
- Supported development of an AI-powered preprocessing framework for whole-slide image dataset quality improvement.
- Contributed to pathology image preprocessing workflows involving artifact filtering, tile extraction, and quality control.
- Helped evaluate preprocessing strategies for improving model reasoning in computational pathology.
- Contributed to research dissemination through Stanford Pathology Research Retreat and manuscript preparation.
Research relevance:
- Artifact-aware WSI preprocessing
- Dataset quality control
- Reliable model reasoning
- Computational pathology
- Whole-slide image machine learning
Related work:
Rathinaraja Jeyaraj, Barathi Subramanian, Jeanne Shen, et al., “MUFASA: An Information Utility-Aware Preprocessing Framework for Reliable Model Reasoning in Computational Pathology,” in preparation.