Mass detection in mammograms using pre-trained deep learning models

Richa Agarwal; Oliver Diaz; Xavier Lladó; Robert Martí

doi:10.1117/12.2317681

6 July 2018 Mass detection in mammograms using pre-trained deep learning models

Richa Agarwal, Oliver Diaz, Xavier Lladó, Robert Martí

Proceedings Volume 10718, 14th International Workshop on Breast Imaging (IWBI 2018); 107181F (2018) https://doi.org/10.1117/12.2317681
Event: The Fourteenth International Workshop on Breast Imaging, 2018, Atlanta, Georgia, United States

Abstract

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.

Citation Download Citation

Richa Agarwal, Oliver Diaz, Xavier Lladó, and Robert Martí "Mass detection in mammograms using pre-trained deep learning models", Proc. SPIE 10718, 14th International Workshop on Breast Imaging (IWBI 2018), 107181F (6 July 2018); https://doi.org/10.1117/12.2317681

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