Project 2.1 - Weakly Labeled Learning
We focus on deep learning with large amounts of images that are only weakly labeled (e.g. only overall diagnosis or treatment outcome is available). We develop techniques that exploit a small set of images with detailed annotations and a large pool of weakly or completely unlabeled data. We exploit shared representations between learning tasks with different localization levels and use active learning where medical experts are asked for feedback on automatically selected cases.
|Prof.dr. Josien Pluim Eindhoven University of Technology email@example.com|
|Dr. Marleen de Bruijne Erasmus Medical Center firstname.lastname@example.org|
|Dr. Mitko Veta Eindhoven University of Technology M.Veta@tue.nl|
|Suzanne Wetstein Eindhoven University of Technology email@example.com|
|Friso Heslinga Eindhoven University of Technology firstname.lastname@example.org|
|Gerda Bortsova Erasmus Medical Center email@example.com|