A transfer learning approach for classification of clinical significant prostate cancers from mpMRI scans

Quan Chen, Xiang Xu, Shiliang Hu, Xiao Li, Qing Zou, Yunpeng Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

28 Scopus citations


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.

Original languageEnglish
Title of host publicationMedical Imaging 2017
Subtitle of host publicationComputer-Aided Diagnosis
EditorsNicholas A. Petrick, Samuel G. Armato
ISBN (Electronic)9781510607132
StatePublished - 2017
EventMedical Imaging 2017: Computer-Aided Diagnosis - Orlando, United States
Duration: Feb 13 2017Feb 16 2017

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2017: Computer-Aided Diagnosis
Country/TerritoryUnited States


  • Computer aided diagnosis (CADx)
  • Convolution neural Network (CNN)
  • Deep learning neural network (DNN)
  • Multiparameter MRI (mpMRI)
  • Prostate cancer
  • ProstateX challenge
  • Transfer learning
  • VGG

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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