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Tuesday, October 15 • 10:45am - 11:00am
Application of Deep Learning to Automated Diagnosis of Lymphoma with Digital Pathology Images

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WATCH VIDEO​​​

Recent studies have shown promising results in using Deep Learning to detect malignancy in whole slide imaging.

Studies had been limited to just predicting positive or negative finding for a specific malignancy. We attempted to build a diagnostic model for four diagnostic categories of lymphoma. 

Our Deep Learning software, a convolutional neural network, was written in Python language. We obtained digital whole-slide images of 128 cases including 32 cases for each diagnostic category. Four sets of 5 representative images, 40x40 pixels in dimension, were taken for each case. For each test set of 5 images, the predicted diagnosis was combined from prediction of five images. The test results showed excellent diagnostic accuracy at 95% for image-by-image prediction and at 100% for set-by-set prediction. 

This preliminary study provided a proof of concept for incorporating automated lymphoma diagnostic screen into future pathology workflow to augment the pathologists’ productivity.

Speakers
AN

Andy Nguyen

Presenter, University of Texas Health Science Center at Houston
HE

Hanadi El Achi

University of Texas Health Science Center at Houston
TB

Tatiana Belousova

University of Texas Health Science Center at Houston
LC

Lei Chen

University of Texas Health Science Center at Houston
AW

Amer Wahed

University of Texas Health Science Center at Houston
IW

Iris Wang

University of Texas Health Science Center at Houston
ZH

Zhihong Hu

University of Texas Health Science Center at Houston
ZK

Zeyad Kanaan

University of Texas Health Science Center at Houston
AR

Adan Rios

University of Texas Health Science Center at Houston



Tuesday October 15, 2019 10:45am - 11:00am CDT
BRC 280

Attendees (4)