A Variational Model for Brain Tumor Segmentation: Deep Learning Based Parameter OptimizationMS5

We present a new variational model for saliency detection in images and its application to brain tumor segmentation. Incorporating a saliency term to a classical Total Variation based restoration functional this model is able to discriminate what is relevant (salient) from the background, resulting into a non-convex and non-smooth problem. To optimize the parameters of the proposed energy functional we introduce a Deep Learning framework using available knowledge from the specific application.

This presentation is part of Minisymposium “MS5 - Learning and adaptive approaches in image processing (2 parts)
organized by: Kostas Papafitsoros (Weierstrass Institute Berlin) , Michael Hintermüller (Humboldt University and Weierstrass Institute Berlin) .

Adrián Martín (Universitat Pompeu Fabra)
Emanuele Schiavi (Universidad Rey Juan Carlos)
Ivan Ramirez (Universidad Rey Juan Carlos)
deep learning, image segmentation, machine learning, nonconvex problems, nonlinear optimization, parameter learning, partial differential equation models