Enhancing Breast Cancer Classification via Information and Multi-model Integration

Abstract

The integration of different sources of information for proper classification is of utter importance, specially in the biomedical field. Many different sources of information can be collected from a patient and they all may contribute to an accurate diagnosis. For example in cancer disease these can include gene expression (RNA-Seq) or Tissue Slide Imaging, however, their integration in order to correctly train a classification model is not straightforward. Making use of Whole-Slide-Images, this work presents a novel information integration model when different sources of data from a patient are available, named as Multi-source integration model (MSIM). Using two different Convolutional Neural Networks architectures and a Feed Forward Neural Network, the potential of a multi-model integration process which combines the information of different sources is introduced and its results are presented for Breast Cancer classification.