Abstract
Prostate cancer diagnosis involves the highly subjective and time-consuming Gleason grading process. This paper proposes the use of Max-Margin Conditional Random Fields (CRFs) towards the aim of creating an automatic computer-aided diagnosis system. Unlike previous methods, this approach enables us to fuse information from multiple classifiers while leveraging CRFs to model spatial dependencies. We perform grading on superpixels which reduce redundancy and the size of data. Probabilistic outputs from independent classifiers are passed as input to a Max-Margin CRF, which then performs structured prediction on the biopsy core, segmenting the image into regions of benign tissue, Gleason grade 3 adenocarcinoma and Gleason grade 4 adenocarcinoma. The system achieves an accuracy of 83.0% with accuracies of 83.6%, 86.9% and 77.1% reported for benign, grade 3 and grade 4 classes respectively.
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Jacobs, J.G., Panagiotaki, E., Alexander, D.C. (2014). Gleason Grading of Prostate Tumours with Max-Margin Conditional Random Fields. In: Wu, G., Zhang, D., Zhou, L. (eds) Machine Learning in Medical Imaging. MLMI 2014. Lecture Notes in Computer Science, vol 8679. Springer, Cham. https://doi.org/10.1007/978-3-319-10581-9_11
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DOI: https://doi.org/10.1007/978-3-319-10581-9_11
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