Cross-database mammographic image analysis through unsupervised domain adaptation

Abstract

World Health Organization report shows 519,000 deaths due to breast cancer in 2014 and it was much more in 2008. Therefore, it is required to take early steps in detection and diagnosis of breast cancer to decrease the associated death rate. Computer Aided Diagnosis (CAD) is useful in mass screening of breast cancer datasets. Data mining and machine learning technologies have already achieved significant success in many knowledge engineering areas including classification, regression and clustering, and most recently, have been employed to assist the diagnosis of cancers with promising outcomes. Traditional machine learning models are characterized by training and testing data with the same input feature space and data distribution. But when distribution changes, most machine learning models need to be modified or rebuilt from scratch to work on newly collected data. In many real world applications, it is expensive or impossible to recollect the needed data and rebuild the models. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred as Transfer Learning. In this paper, we explore the usage of transfer learning, specially, unsupervised domain adaptation for breast cancer diagnosis to address the issues of fewer training data on target image dataset. On the strength of recent developed deep descriptors, we are able to adapt recent transfer learning methodologies, e.g., TCA (Transfer Component Analysis), CORAL (Correlation Alignment), BDA(Balanced Distribution Adaptation) to breast cancer diagnosis across multiple mammographic image databases including CBIS-DDSM, InBreast, MIAS, etc, and evaluate their performance. Experiments demonstrate that, without any labels in the target database, transfer learning is able to help improve the classification accuracy.