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Proceedings Paper

A transfer learning approach for classification of clinical significant prostate cancers from mpMRI scans
Author(s): Quan Chen; Xiang Xu; Shiliang Hu; Xiao Li; Qing Zou; Yunpeng Li
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Paper Abstract

Deep learning has shown a great potential in computer aided diagnosis. However, in many applications, large dataset is not available. This makes the training of a sophisticated deep learning neural network (DNN) difficult. In this study, we demonstrated that with transfer learning, we can quickly retrain start-of-the-art DNN models with limited data provided by the prostateX challenge. The training data consists of 330 lesions, only 78 were clinical significant. Efforts were made to balance the data during training. We used ImageNet pre-trained inceptionV3 and Vgg-16 model and obtained AUC of 0.81 and 0.83 respectively on the prostateX test data, good for a 4th place finish. We noticed that models trained for different prostate zone has different sensitivity. Applying scaling factors before merging the result improves the AUC for the final result.

Paper Details

Date Published: 16 March 2017
PDF: 4 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101344F (16 March 2017); doi: 10.1117/12.2279021
Show Author Affiliations
Quan Chen, Univ. of Virginia (United States)
Xiang Xu, SkyData InfoTech (China)
Shiliang Hu, SkyData InfoTech (China)
Xiao Li, Affiliated Cancer Hospital of Nanjing Medical Univ. (China)
Qing Zou, Affiliated Cancer Hospital of Nanjing Medical Univ. (China)
Yunpeng Li, SkyData InfoTech (China)

Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato III; Nicholas A. Petrick, Editor(s)

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