An interactive learning approach to histology image segmentation
Michael Derde, Laura Antanas, Luc De Raedt, Fabián Güiza · Lirias (KU Leuven) · 2012
Histology image analysis using computer-aided diagnosis systems has become increasingly important during the last years. One reason is the need to alleviate the heavy workload of medical experts. In this paper, we introduce a general purpose framework which is able to solve histology analysis problems that are not restricted to a specific type of tissue or task, exploit local information in microscopical images, interact with medical experts and interatively consider direct user feedback. The framework is general enough to learn models that can adapt to several learning tasks and can detect several types of medical interest regions. We evaluate our framework on real-world datasets collected from patients in the intensive care unit. We considerably outperform image processing techniques commonly used in such medical imaging tasks.