2018/11/09 by Edwin Yuan, Yuan, Edwin, Junkyo Suh +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #AI in cancer detection #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging for Blood Diseases #FOS: Biological sciences #FOS: Computer and information sciences #Quantitative Methods (q-bio.QM) #cs.CV #q-bio.QM
paper · pdf · doi:10.48550/arxiv.1811.03815
9 pages, 8 figures
arxiv created 2018/11/09 · openalex publication_date 2018/11/09 · arxiv updated 2018/11/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.