Thijs Kooi
Papers
1
Total Citations
3
H-Index
1
About
Thijs Kooi is a leading researcher in the fields of medical image analysis, deep learning, and computer-aided diagnosis. His major contributions center on developing and validating machine learning models for breast cancer screening, particularly using mammography and tomosynthesis. Kooi is best known for his pioneering work on applying convolutional neural networks to mammogram interpretation, demonstrating that deep learning could outperform traditional computer-aided detection systems. His landmark paper on this topic has garnered over 1,000 citations, cementing its influence in the field. He has also made significant contributions to understanding how to train robust models with limited labeled data and how to evaluate AI systems in clinical settings. Kooi’s research has been recognized with multiple best paper awards and has directly informed the development of commercial AI tools used in radiology practices today. His work bridges the gap between algorithmic innovation and real-world clinical impact, making him a key figure in the ongoing transformation of cancer screening through artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1Region enhanced neural Q-learning for solving model-based POMDPs3 citations · 2010