  Breast cancer is one of the leading causes of female mortality in the world.
This can be reduced when diagnoses are performed at the early stages of
progression. Further, the efficiency of the process can be significantly
improved with computer aided diagnosis. Deep learning based approaches have
been successfully applied to achieve this. One of the limiting factors for
training deep networks in a supervised manner is the dependency on large
amounts of expert annotated data. In reality, large amounts of unlabelled data
and only small amounts of expert annotated data are available. In such
scenarios, transfer learning approaches and self-supervised learning (SSL)
based approaches can be leveraged. In this study, we propose a novel
self-supervision pretext task to train a convolutional neural network (CNN) and
extract domain specific features. This method was compared with deep features
extracted using pre-trained CNNs such as DenseNet-121 and ResNet-50 trained on
ImageNet. Additionally, two types of patch-combination methods were introduced
and compared with majority voting. The methods were validated on the BACH
microscopy images dataset. Results indicated that the best performance of 99%
sensitivity was achieved for the deep features extracted using ResNet50 with
concatenation of patch-level embedding. Preliminary results of SSL to extract
domain specific features indicated that with just 15% of unlabelled data a high
sensitivity of 94% can be achieved for a four class classification of
microscopy images.
