Support vector machines for prostate lesion classification

Andy Kitchen; Jarrel Seah

doi:10.1117/12.2277120

3 March 2017 Support vector machines for prostate lesion classification

Andy Kitchen, Jarrel Seah

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 1013427 (2017) https://doi.org/10.1117/12.2277120
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Support vector machines (SVM) are applied to the problem of prostate lesion classification for the SPIE ProstateX Challenge 2016, achieving a score of 0.82 AUC on held-out test data. Square 5mm transverse image patches are extracted around each lesion center from aligned MRI scans. Three MRI modalities are simultaneously analyzed: T2-weighted, apparent diffusion coefficient (ADC) and volume transfer constant (K^trans). Extracted patches are used to train a binary classifier to predict clinical significance. The machine learning algorithm is trained on 76 positive cases and 254 negative cases (330 total) from the challenge. The method is conceptually simple, trains in a few seconds and yields competitive results.

Citation Download Citation

Andy Kitchen and Jarrel Seah "Support vector machines for prostate lesion classification", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013427 (3 March 2017); https://doi.org/10.1117/12.2277120

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