Conference proceeding
A Deep Semantic Mobile Application for Thyroid Cytopathology
MEDICAL IMAGING 2016: PACS AND IMAGING INFORMATICS: NEXT GENERATION AND INNOVATIONS, v 9789, pp 97890A-97890A-9
01 Jan 2016
Featured in Collection : UN Sustainable Development Goals @ Drexel
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 bene fits of our framework through both quantitative and qualitative results.
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Details
- Title
- A Deep Semantic Mobile Application for Thyroid Cytopathology
- Creators
- Edward Kim - Villanova UniversityMiguel Cortre-Real - Villanova UniversityZubair Baloch - University of Pennsylvania Health System
- Contributors
- J Zhang (Editor)T S Cook (Editor)
- Publication Details
- MEDICAL IMAGING 2016: PACS AND IMAGING INFORMATICS: NEXT GENERATION AND INNOVATIONS, v 9789, pp 97890A-97890A-9
- Series
- Proceedings of SPIE
- Publisher
- Spie-Int Soc Optical Engineering
- Number of pages
- 9
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:000378538300007
- Scopus ID
- 2-s2.0-84976273604
- Other Identifier
- 991021884690304721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Optics
- Radiology, Nuclear Medicine & Medical Imaging