Quality Evaluation for AI-assisted Medical Image Analysis

This research focuses on advancing image quality evaluation techniques to enhance the performance and reliability of AI-assisted medical image analysis across diverse imaging modalities. This includes whole slide images (WSIs) in digital pathology, endoscopy images, and other clinical imaging data where quality variations can significantly impact diagnostic outcomes. This project aims to develop more robust AI models, improve data preprocessing pipelines, and contribute to more accurate and dependable clinical decision-making by systematically assessing issues such as blurriness, artifacts, noise, and color inconsistencies.