title: Large scale deep learning for computer aided detection of mammographic lesions.
abstract: Recent advances in machine learning yielded new techniques to train deep neural networks, which resulted in highly successful applications in many pattern recognition tasks such as object detection and speech recognition. In this paper we provide a head-to-head comparison between a state-of-the art in mammography CAD system, relying on a manually designed feature set and a Convolutional Neural Network (CNN), aiming for a system that can ultimately read mammograms independently. Both systems are trained on a large data set of around 45,000 images and results show the CNN outperforms the traditional CAD system at low sensitivity and performs comparable at high sensitivity. We subsequently investigate to what extent features such as location and patient information and commonly used manual features can still complement the network and see improvements at high specificity over the CNN especially with location and context features, which contain information not available to the CNN. Additionally, a reader study was performed, where the network was compared to certified screening radiologists on a patch level and we found no significant difference between the network and the readers.
authors: Diagnostic Image Analysis Group, Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: thijs.kooi@radboudumc.nl.
authors: Thijs
authors: T
authors: Kooi
authors: Diagnostic Image Analysis Group, Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands.
authors: Geert
authors: G
authors: Litjens
authors: Diagnostic Image Analysis Group, Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands.
authors: Bram
authors: B
authors: van Ginneken
authors: Diagnostic Image Analysis Group, Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands.
authors: Albert
authors: A
authors: Gubern-Mérida
authors: Diagnostic Image Analysis Group, Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands.
authors: Clara I
authors: CI
authors: Sánchez
authors: Diagnostic Image Analysis Group, Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands.
authors: Ritse
authors: R
authors: Mann
authors: Department of Radiology, University Medical Centre Amsterdam, Amsterdam, The Netherlands.
authors: Ard
authors: A
authors: den Heeten
authors: Diagnostic Image Analysis Group, Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands.
authors: Nico
authors: N
authors: Karssemeijer
mesh_terms: D001940:Breast / Q000000981:diagnostic imaging* / Q000473:pathology*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D006801:Humans; D000069550:Machine Learning; D008327:Mammography / Q000379:methods*; D016571:Neural Networks, Computer; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity
keywords: Breast cancer; Computer aided detection; Convolutional neural networks; Deep learning; Machine learning; Mammography