Elastic deformations for data augmentation in breast cancer mass detection

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Castro, E (Author) Other; Jaime S Cardoso (Author) FEUP; Pereira, JC (Author) Other

Conference proceedings International

Title: 2018 IEEE EMBS International Conference on Biomedical & Health Informatics, BHI 2018, Las Vegas, NV, USA, March 4-7, 2018

Pages: 230-234

2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018

4 March 2018 through 7 March 2018

Indexing

Scopus - 14 Citations

Other information

Authenticus ID: P-00N-W6T

DOI: 10.1109/bhi.2018.8333411

Abstract (EN): Two limitations hamper performance of deep architectures for classification and/or detection in medical imaging: (i) the small amount of available data, and (ii) the class imbalance scenario. While millions of labeled images are available today to build classification tools for natural scenes, the amount of available annotated data for automatic breast cancer screening is limited to a few thousand images, at best. We address these limitations with a method for data augmentation, based on the introduction of random elastic deformations on images of mammograms. We validate this method on three publicly available datasets. Our proposed Convolutional Neural Network (CNN) archi-tecture is trained for mass classification - in a conventional way - , and then used in the more interesting problem of mass detection in full mammograms by transforming the CNN into a Fully Convolutional Network (FCN). © 2018 IEEE.

Language: English

Type (Professor's evaluation): Scientific