Incremental learning for segmentation in medical images

Hand-coded vision systems are problematic in complex medical domains and are hard to change as new information emerges. Knowledge-engineering and machine learning approaches to intelligent vision systems also face learning bottlenecks. We have developed an approach to engineering vision systems, which allowed the user to make incremental changes to refine the performance of the system and address these limitations. A medical image segmentation system was built using this approach. In only a few hours of training, the system was able to exceed the performance of a similar hand-coded system built over a period of three months