Abstract
We present a novel approach for classifying the Gleason score for prostate tumours based on MRI data. Proposed approach uses three scores: 2, 3 and 4–5 (representing Gleason scores 4 and 5 as one single class). Patches are extracted from annotated MRI data for each of the class. Raw image patches have been used as features, instead of extracting manual hand-crafted features. Each patch is encoded using a dictionary and the encoded feature vector is then used for classification. A voting-based encoding approach is used to transform data from the image domain to more discriminative class-specific representations. Initial investigation demonstrated excellent results (Classification Accuracy equal to 85% and Area Under the ROC Curve (AUC) of 0.932) for 3-class Gleason score classification for prostate tumours.
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Suhail, Z., Mahmood, A., Wang, L., Malcolm, P.N., Zwiggelaar, R. (2018). A Voting-Based Encoding Technique for the Classification of Gleason Score for Prostate Cancers. In: Nixon, M., Mahmoodi, S., Zwiggelaar, R. (eds) Medical Image Understanding and Analysis. MIUA 2018. Communications in Computer and Information Science, vol 894. Springer, Cham. https://doi.org/10.1007/978-3-319-95921-4_9
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