Abstract
Computer-Aided Detection/Diagnosis (CAD) tools were created to assist the detection and diagnosis of early stage cancers, decreasing false negative rate and improving radiologists’ efficiency. Convolutional Neural Networks (CNNs) are one example of deep learning algorithms that proved to be successful in image classification. In this paper we aim to study the application of CNNs to the classification of lesions in mammograms. One major problem in the training of CNNs for medical applications is the large dataset of images that is often required but seldom available. To solve this problem, we use a transfer learning approach, which is based on three different networks that were pre-trained on the Imagenet dataset. We then investigate the performance of these pre-trained CNNs and two types of image normalization to classify lesions in mammograms. The best results were obtained using the Caffe reference model for the CNN with no image normalization.
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Acknowledgments
This work was supported by National Funding from the FCT – Fundação para a Ciência e a Tecnologia, through the UID/EEA/50008/2013 Project. The GTX Titan X used in this research was donated by the NVIDIA Corporation.
The database used in this work was a courtesy of MA Guevara and coauthors, Breast Cancer Digital Repository Consortium.
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Perre, A., Alexandre, L.A., Freire, L.C. (2018). Lesion Classification in Mammograms Using Convolutional Neural Networks and Transfer Learning. In: Tavares, J., Natal Jorge, R. (eds) VipIMAGE 2017. ECCOMAS 2017. Lecture Notes in Computational Vision and Biomechanics, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-68195-5_40
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DOI: https://doi.org/10.1007/978-3-319-68195-5_40
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