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Proceedings Paper

A deep semantic mobile application for thyroid cytopathology
Author(s): Edward Kim; Miguel Corte-Real; Zubair Baloch
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Paper Abstract

Cytopathology is the study of disease at the cellular level and often used as a screening tool for cancer. Thyroid cytopathology is a branch of pathology that studies the diagnosis of thyroid lesions and diseases. A pathologist views cell images that may have high visual variance due to different anatomical structures and pathological characteristics. To assist the physician with identifying and searching through images, we propose a deep semantic mobile application. Our work augments recent advances in the digitization of pathology and machine learning techniques, where there are transformative opportunities for computers to assist pathologists. Our system uses a custom thyroid ontology that can be augmented with multimedia metadata extracted from images using deep machine learning techniques. We describe the utilization of a particular methodology, deep convolutional neural networks, to the application of cytopathology classification. Our method is able to leverage networks that have been trained on millions of generic images, to medical scenarios where only hundreds or thousands of images exist. We demonstrate the benefits of our framework through both quantitative and qualitative results.

Paper Details

Date Published: 5 April 2016
PDF: 9 pages
Proc. SPIE 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations, 97890A (5 April 2016); doi: 10.1117/12.2216468
Show Author Affiliations
Edward Kim, Villanova Univ. (United States)
Miguel Corte-Real, Villanova Univ. (United States)
Zubair Baloch, The Univ. of Pennsylvania Health System (United States)


Published in SPIE Proceedings Vol. 9789:
Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations
Jianguo Zhang; Tessa S. Cook, Editor(s)

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