Fusion in breast cancer histology classification

• Authors: Juan Vizcarra, Emory University; Ryan Place, Georgia Institute of Technology; Li Tong, Emory University; David Gutman, Emory University; Dongmei Wang, Emory University
• Journal Title: ACM-BCB 2019 - Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
• Volume: Volume 2019
• Publisher: ACM Publishing | 2019-09-04, Pages 485-493
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1145/3307339.3342166
• Subjects: Health Sciences, Oncology; Biology, Neuroscience; Engineering, Biomedical
• Keywords: Fusion; Histology; SURF; breast cancer; deep learning; support vector machines

Abstract

Breast cancer is a deadly disease that affects millions of women worldwide. The International Conference on Image Analysis and Recognition in 2018 presents the BreAst Cancer Histology (ICIAR2018 BACH) image data challenge that calls for computer tools to assist pathologists and doctors in the clinical diagnosis of breast cancer subtypes. Using the BACH dataset, we have developed an image classification pipeline that combines both a shallow learner (support vector machine) and a deep learner (convolutional neural network). The shallow learner and deep learners achieved moderate accuracies of 79% and 81% individually. When being integrated by fusion algorithms, the system outperformed any individual learner with the highest accuracy as 92%. The fusion presents big potential for improving clinical design support.