Abstract
In this work, we investigated the potential of a recently proposed parameter learning algorithm for Conditional Random Fields (CRFs). Parameters of a pairwise CRF are estimated via a stochastic subgradient descent of a max-margin learning problem. We compared the performance of our brain tumor segmentation method using parameter learning to a version using hand-tuned parameters. Preliminary results on a subset of the BRATS2015 training set show that parameter learning leads to comparable or even improved performance. In addition, we also performed experiments to study the impact of the composition of training data on the final segmentation performance. We found that models trained on mixed data sets achieve reasonable performance compared to models trained on stratified data.
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Notes
- 1.
Fast-PD requires \(B_{i,j}\) to define a semi-metric.
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This project has received funding from the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement No. 600841.
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Meier, R., Karamitsou, V., Habegger, S., Wiest, R., Reyes, M. (2016). Parameter Learning for CRF-Based Tissue Segmentation of Brain Tumors. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2015. Lecture Notes in Computer Science(), vol 9556. Springer, Cham. https://doi.org/10.1007/978-3-319-30858-6_14
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