A Mixture of Views Network with Applications to the Classification of Breast Microcalcifications
This work addresses the classification of breast microcalcifications for medical diagnosis, presenting an incremental improvement over existing fusion methods.
The paper tackled the problem of classifying breast microcalcifications as benign or malignant using multi-view mammography data by proposing a Mixture of Views neural network that combines decisions based on view relevance, and it outperformed previous fusion methods on a large dataset from DDSM.
In this paper we examine data fusion methods for multi-view data classification. We present a decision concept which explicitly takes into account the input multi-view structure, where for each case there is a different subset of relevant views. This data fusion concept, which we dub Mixture of Views, is implemented by a special purpose neural network architecture. It is demonstrated on the task of classifying breast microcalcifications as benign or malignant based on CC and MLO mammography views. The single view decisions are combined by a data-driven decision, according to the relevance of each view in a given case, into a global decision. The method is evaluated on a large multi-view dataset extracted from the standardized digital database for screening mammography (DDSM). The experimental results show that our method outperforms previously suggested fusion methods.