Computer-Aided Imaging Technique Could Reduce Risk of Second Breast Cancer Surgery

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By Lehigh University

June 8, 2017

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Sample optical coherence microscopy images (first row) and corresponding histology images (second row) of human breast tissue.

A computer-aided diagnostic technique developed by Lehigh University researchers combines imaging technology with the latest advances in artificial intelligence to detect, in real time, the difference between cancerous and benign cells.

Credit: Sunhua Wan, Hsiang-Chieh Lee, Xiaolei Huang, Ting Xu, Tao Xu, Xianxu Zeng, Zhan Zhang, Yuri Sheikine, James L. Connolly, James G. Fujimoto, Chao Zhou

Researchers at Lehigh University have developed a computer-aided diagnostic technique that combines imaging technology with the latest advances in artificial intelligence to detect in real time the difference between cancerous and benign cells.

“The idea is that one day, if this technique could be used during surgery, it could complement the histopathology, potentially reducing the need for a second breast cancer surgery,” says Lehigh professor Chao Zhou.

He notes a feasibility study found the new technique achieved a classification accuracy of more than 90%.

Zhou says the method uses a new application of an imaging technique called optical coherence microscopy (OCM) as a breast cancer diagnostic. Features extracted from the OCM images are used to train the computer system to automatically identify different tissue types.

After examining multiple types of texture features, the researchers found Local Binary Pattern features worked best for classifying tissues imaged via OCM.

From Lehigh University
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Abstracts Copyright © 2017 Information Inc., Bethesda, Maryland, USA


 

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from Communications of the ACM: Artificial Intelligence http://ift.tt/2s8hXD1

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