Revealing associations between histological features and individual traits

Breast cancer stands as a prominent global health concern, consistently ranking among the most prevalent types of cancer affecting women. In this work, the focus is put on invasive ductal carcinoma and invasive lobular carcinoma, which are two most common types of breast cancer. Transfer learning techniques are applied to implement a Convolutional Neural Network (CNN) architecture that accurately classifies histological images accurately based on the subtype of cancer. Next, an integrative analysis is done with the artificial descriptors obtained from the CNN and gene expression RNA-Seq data to see the associations between the two datasets. Finally, a gene set enrichment analysis is performed with the aim of providing a biological interpretation to the artificial descriptors obtained from the CNN, by correlating them with biological pathways relevant to these two subtypes of breast cancer.