  In this paper, we develop a complete pipeline for stain normalization,
segmentation, and classification of nuclei in hematoxylin and eosin (H&E)
stained breast cancer histopathology images. In the first step, we use a
CNN-based stain transfer technique to normalize the staining characteristics of
(H&E) images. We then train a neural network to segment images of nuclei from
the H&E images. Finally, we train an Information Maximizing Generative
Adversarial Network (InfoGAN) to learn visual representations of different
types of nuclei and classify them in an entirely unsupervised manner. The
results show that our proposed CNN stain normalization yields improved visual
similarity and cell segmentation performance compared to the conventional
SVD-based stain normalization method. In the final step of our pipeline, we
demonstrate the ability to perform fully unsupervised clustering of various
breast histopathology cell types based on morphological and color attributes.
In addition, we quantitatively evaluate our neural network - based techniques
against various quantitative metrics to validate the effectiveness of our
pipeline.
