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

Paper Details

Date Published: 18 March 2019
PDF: 6 pages
Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 1095611 (18 March 2019); doi: 10.1117/12.2512963
Show Author Affiliations
Dig Vijay Kumar Yarlagadda, Univ. of Missouri-Kansas City (United States)
Praveen Rao, Univ. of Missouri-Kansas City (United States)
Deepthi Rao, Univ. of Missouri-Columbia (United States)
Ossama Tawfik, Saint Luke's Hospital of Kansas City (United States)


Published in SPIE Proceedings Vol. 10956:
Medical Imaging 2019: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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