Weakly Supervised Group-Wise Model Learning Based on Discrete Optimization

Authors:
René Donner;Horst Wildenauer;Horst Bischof;Georg Langs
Affiliations:
Computational Image Analysis and Radiology Lab, Department of Radiology, Medical University of Vienna, Austria and Institute for Computer Graphics and Vision, Graz University of Technology, Austri ...;Pattern Recognition and Image Processing Group, Vienna University of Technology, Austria;Institute for Computer Graphics and Vision, Graz University of Technology, Austria;Computational Image Analysis and Radiology Lab, Department of Radiology, Medical University of Vienna, Austria
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

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Abstract

In this paper we propose a method for the weakly supervised learning of sparse appearance models from medical image data based on Markov random fields (MRF) . The models are learnt from a single annotated example and additional training samples without annotations. The approach formulates the model learning as solving a set of MRFs. Both the model training and the resulting model are able to cope with complex and repetitive structures. The weakly supervised model learning yields sparse MRF appearance models that perform equally well as those trained with manual annotations, thereby eliminating the need for tedious manual training supervision. Evaluation results are reported for hand radiographs and cardiac MRI slices.