A system for one-shot learning of cervical cancer cell classification in histopathology images

Dig Vijay Kumar Yarlagadda, Praveen Rao, Deepthi S. Rao, Ossama Tawfik

Abstract: Convolutional neural networks (CNNs) have been popularly used to solve the problem of cell/nuclei classification and segmentation in histopathology images. Despite their pervasiveness, CNNs are fine-tuned on specific, large and labeled datasets as these datasets are hard to collect and annotate. However, this is not a scalable approach. In this work, we aim to gain deeper insights into the nature of the problem. We used a cervical cancer dataset with cells labeled into four classes by an expert pathologist. By employing pre-training on this dataset, we propose a one-shot learning model for cervical cell classification in histopathology tissue images. We extract regional maximum activation of convolutions (R-MAC) global descriptors and train a one-shot learning memory module with the goal of using it for various cancer types and eliminate the need for expensive, difficult to collect, large, labeled whole slide image (WSI) datasets. Our model achieved 94.6% accuracy in detecting the four cell classes on the test dataset. Further, we present our analysis of the dataset and features to better understand and visualize the problem in general.