Pre-training lays the foundation for recent successes in radiograph analysis supported by deep learning. It learns transferable image representations by conducting large-scale fully- or self-supervised learning on a source domain; however, supervised pre-training requires a complex and labour-intensive two-stage human-assisted annotation process, whereas self-supervised learning cannot compete with the supervised paradigm. To tackle these issues, we propose a cross-supervised methodology called reviewing free-text reports for supervision (REFERS), which acquires free supervision signals from the original radiology reports accompanying the radiographs. The proposed approach employs a vision transformer and is designed to learn joint representations from multiple views within every patient study. REFERS outperforms its transfer learning and self-supervised learning counterparts on four well-known X-ray datasets under extremely limited supervision. Moreover, REFERS even surpasses methods based on a source domain of radiographs with human-assisted structured labels; it therefore has the potential to replace canonical pre-training methodologies.
Professor Yizhou Yu received the PhD degree from University of California at Berkeley. He is a chair professor with the University of Hong Kong, and was a faculty member with the University of Illinois at Urbana-Champaign for twelve years. He is an ACM Fellow and IEEE Fellow, and has been named World’s Top 2% Scientists by Stanford University. He has been an associate editor of many international journals, and has also served on the program committee of many leading international conferences, including CVPR and SIGGRAPH. His research interests include AI foundation models, AI-based content generation, AI for medicine, and computer vision.
Clean water and clean air are vital for public health. This project focuses on developing high-efficiency and environmentally sustainable filters for removing harmful air/water pollutants. The team has developed novel architectures and functionalities for the filters to achieve high permeance, high removal efficiency, and excellent reusability.
