Automatic Microcalcification Detection in Multi-vendor Mammography Using Convolutional Neural Networks

TL;DR: This was the first study to use a deep learning strategy for the detection of microcalcifications in mammograms, and significantly higher mean sensitivities were obtained with the CNN on the mammograms of each individual manufacturer compared to the cascade classifier.

Abstract: Convolutional neural networks CNNs have shown to be powerful for classification of image data and are increasingly used in medical image analysis. Therefore, CNNs might be very suitable to detect microcalcifications in mammograms. In this study, we have configured a deep learning approach to fulfill this task. To overcome the large class imbalance between pixels belonging to microcalcifications and other breast tissue, we applied a hard negative mining strategy where two CNNs are used. The deep learning approach was compared to a current state-of-the-art method for the detection of microcalcifications: the cascade classifier. Both methods were trained on a large training set including 11,711 positive and 27 million negative samples. For testing, an independent test set was configured containing 5,298 positive and 18 million negative samples. The mammograms included in this study were acquired on mammography systems from three manufactures: Hologic, GE, and Siemens. Receiver operating characteristics analysis was carried out. Over the whole specificity range, the CNN approach yielded a higher sensitivity compared to the cascade classifier. Significantly higher mean sensitivities were obtained with the CNN on the mammograms of each individual manufacturer compared to the cascade classifier in the specificity range of 0 to 0.1. To our knowledge, this was the first study to use a deep learning strategy for the detection of microcalcifications in mammograms.