Neural Stain Normalization and Unsupervised Classification of Cell Nuclei in Histopathological Breast Cancer Images
This work addresses the problem of automating analysis in histopathological breast cancer images for medical diagnosis, but it is incremental as it builds on existing neural network techniques.
The paper tackled stain normalization and unsupervised classification of cell nuclei in breast cancer histopathology images, resulting in improved visual similarity and segmentation performance compared to conventional methods, with quantitative validation of the pipeline.
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.