Automatic mass detection in mammograms using deep convolutional neural networks

Richa Agarwal; Oliver Diaz; Xavier Lladó; Moi Hoon Yap; Robert Martí

doi:10.1117/1.JMI.6.3.031409

20 February 2019 Automatic mass detection in mammograms using deep convolutional neural networks

Richa Agarwal, Oliver Diaz, Xavier Lladó, Moi Hoon Yap, Robert Martí

Author Affiliations +

Journal of Medical Imaging, Vol. 6, Issue 3, 031409 (February 2019). https://doi.org/10.1117/1.JMI.6.3.031409

Abstract

With recent advances in the field of deep learning, the use of convolutional neural networks (CNNs) in medical imaging has become very encouraging. The aim of our paper is to propose a patch-based CNN method for automated mass detection in full-field digital mammograms (FFDM). In addition to evaluating CNNs pretrained with the ImageNet dataset, we investigate the use of transfer learning for a particular domain adaptation. First, the CNN is trained using a large public database of digitized mammograms (CBIS-DDSM dataset), and then the model is transferred and tested onto the smaller database of digital mammograms (INbreast dataset). We evaluate three widely used CNNs (VGG16, ResNet50, InceptionV3) and show that the InceptionV3 obtains the best performance for classifying the mass and nonmass breast region for CBIS-DDSM. We further show the benefit of domain adaptation between the CBIS-DDSM (digitized) and INbreast (digital) datasets using the InceptionV3 CNN. Mass detection evaluation follows a fivefold cross-validation strategy using free-response operating characteristic curves. Results show that the transfer learning from CBIS-DDSM obtains a substantially higher performance with the best true positive rate (TPR) of 0.98 ± 0.02 at 1.67 false positives per image (FPI), compared with transfer learning from ImageNet with TPR of 0.91 ± 0.07 at 2.1 FPI. In addition, the proposed framework improves upon mass detection results described in the literature on the INbreast database, in terms of both TPR and FPI.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Richa Agarwal, Oliver Diaz, Xavier Lladó, Moi Hoon Yap, and Robert Martí "Automatic mass detection in mammograms using deep convolutional neural networks," Journal of Medical Imaging 6(3), 031409 (20 February 2019). https://doi.org/10.1117/1.JMI.6.3.031409

Received: 17 September 2018; Accepted: 18 January 2019; Published: 20 February 2019

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