Integrating segmentation information into CNN for breast cancer diagnosis of mammographic masses

• Published in: Computer methods and programs in biomedicine 2021-03, Vol.200, p.105913-105913, Article 105913
• Main Authors: Tsochatzidis, Lazaros, Koutla, Panagiota, Costaridou, Lena, Pratikakis, Ioannis
• Format: Article
• Language: English
• Subjects: Breast Neoplasms - diagnostic imaging; Convolutional neural networks; Deep learning; Diagnosis; Humans; Mammography; Neural Networks, Computer; Segmentation
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2020.105913

•Modification of every convolutional layer in a CNN is addressed which enables the network to not only encounter the original input image but also the corresponding mass segmentation map.•A new loss function is introduced which adds an extra spatially-aware loss function term to the standard cross-entropy, aiming to steer the attention of the network to the mass region during training.•The proposed method is evaluated using both ground-truth and automatically produced segmentation maps. Background and ObjectivesSegmentation of mammographic lesions has been proven to be a valuable source of information, as it can assist in both extracting shape-related features and providing accurate localization of the lesion. In this work, a methodology is proposed for integrating mammographic mass segmentation information into a convolutional neural network (CNN), aiming to improve the diagnosis of breast cancer in mammograms. MethodsThe proposed methodology involves modification of each convolutional layer of a CNN, so that information of not only the input image but also the corresponding segmentation map is considered. Furthermore, a new loss function is introduced, which adds an extra term to the standard cross-entropy, aiming to steer the attention of the network to the mass region, penalizing strong feature activations based on their location. The segmentation maps are acquired either from the provided ground-truth or from an automatic segmentation stage. ResultsPerformance evaluation in diagnosis is conducted on two mammographic mass datasets, namely DDSM-400 and CBIS-DDSM, with differences in quality of the corresponding ground-truth segmentation maps. The proposed method achieves diagnosis performance of 0.898 and 0.862 in terms AUC when using ground-truth segmentation maps and a maximum of 0.880 and 0.860 when a U-Net-based automatic segmentation stage is employed, for DDSM-400 and CBIS-DDSM, respectively. ConclusionsThe experimental results demonstrate that integrating segmentation information into a CNN leads to improved performance in breast cancer diagnosis of mammographic masses.