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.
Mammography is a gold standard imaging modality and is widely used for breast cancer screening. With recent advances in the field of deep learning, the use of deep convolution neural networks (CNNs) in medical image analysis has become very encouraging. The aim of this study is to exploit CNNs for mass detection in mammograms using pre-trained networks. We use the resnet-50 CNN architecture pre-trained with the ImageNet database to perform mass detection on two publicly available image datasets: CBIS-DDSM and INbreast. We demonstrate that the CNN model pretrained using natural image database (ImageNet) can be effectively finetuned to yield better results, compared to randomly initialized models. Further, the benefit of applying transfer learning on a smaller dataset is demonstrated by using the best model obtained from CBIS-DDSM training to finetune on the INbreast database. We analyzed the adaptability of the CNN’s last fully connected (FC) layer and the all convolutional layers to detect masses. The results showed a testing accuracy of 0.92 and an area under the receiver operating characteristic curve (AUC) of 0.98 for the model finetuned on all convolutional layers, while testing accuracy of 0.86 and AUC=0.93 when the model is trained only on the last FC layer.