Interpretable Diagnosis of Breast Cancer from Histological Images Using Siamese Neural Networks

Breast cancer is one of the most widespread causes of women's death worldwide. Successful treatment can be achieved only by the early and accurate tumor diagnosis. The main method of tissue diagnosis taken by biopsy is based on the observation of its significant structures. We propose a novel approach of classifying microscopy tissue images into 4 main cancer classes (normal, benign, In Situ and invasive). Our method is based on comparing and determining the similarity of the new tissue sample with previously by specialists annotated examples that are compiled in the collection with other labeled samples. The most probable class is statistically determined by comparing a new sample with several annotated samples. The usual problem of medical datasets is the small number of training images. We have applied suitable dataset augmentation techniques, using the fact that flipping or mirroring of the sample does not change the information about the diagnosis. Our other contribution is that we show the histopathologist the reason why the algorithm has classified tissue into the particular cancer class by ordering the collection of correctly annotated samples by their similarity to the input sample. Histopathologists can focus on searching for the key structures corresponding to the predicted classes.