New AI algorithms for cost-effective medical image diagnostics

Medical imaging is an important part of modern healthcare, enhancing both the precision, reliability and development of treatment for various diseases. Artificial intelligence has also been widely used to further enhance the process. However, conventional medical image diagnosis employing AI algorithms require large amounts of annotations as supervision signals for model training. To acquire accurate labels for the AI algorithms--radiologists, as part of the clinical routine, prepare radiology reports for each of their patients, followed by annotation staff extracting and confirming structured labels from those reports using human-defined rules and existing natural language processing (NLP) tools. The ultimate accuracy of extracted labels hinges on the quality of human work and various NLP tools.