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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.