Parameter Learning for CRF-Based Tissue Segmentation of Brain Tumors
نویسندگان
چکیده
In this work, we investigate 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. Future work will include training on the complete data set and the use of more elaborate loss functions suitable for brain tumor segmentation.
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