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A deep learning model observer for use in alterative forced choice virtual clinical trials

Samuelson, Frank W., Nishikawa, Robert M., Patel, Mishal, Elangovan, Prem, Wells, Kevin, Halling-Brown, Mark D., Awais, Muhammad, Dance, David R., Young, Kenneth, Mills, G. and Alnowami, Majdi (2018) A deep learning model observer for use in alterative forced choice virtual clinical trials In: SPIE Medical Imaging, 2018, 10-15 Feb 2018, Houston, Texas, United States.

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Abstract

Virtual clinical trials (VCTs) represent an alternative assessment paradigm that overcomes issues of dose, high cost and delay encountered in conventional clinical trials for breast cancer screening. However, to fully utilize the potential benefits of VCTs requires a machine-based observer that can rapidly and realistically process large numbers of experimental conditions. To address this, a Deep Learning Model Observer (DLMO) was developed and trained to identify lesion targets from normal tissue in small (200 x 200 pixel) image segments, as used in Alternative Forced Choice (AFC) studies. The proposed network consists of 5 convolutional layers with 2x2 kernels and ReLU (Rectified Linear Unit) activations, followed by max pooling with size equal to the size of the final feature maps and three dense layers. The class outputs weights from the final fully connected dense layer are used to consider sets of n images in an n-AFC paradigm to determine the image most likely to contain a target. To examine the DLMO performance on clinical data, a training set of 2814 normal and 2814 biopsy-confirmed malignant mass targets were used. This produced a sensitivity of 0.90 and a specificity of 0.92 when presented with a test data set of 800 previously unseen clinical images. To examine the DLMOs minimum detectable contrast, a second dataset of 630 simulated backgrounds and 630 images with simulated lesion and spherical targets (4mm and 6mm diameter), produced contrast thresholds equivalent to/better than human observer performance for spherical targets, and comparable (12 % difference) for lesion targets.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Samuelson, Frank W.
Nishikawa, Robert M.
Patel, Mishal
Elangovan, Prem
Wells, KevinK.Wells@surrey.ac.uk
Halling-Brown, Mark D.
Awais, Muhammad
Dance, David R.
Young, Kenneth
Mills, G.
Alnowami, Majdi
Date : 7 March 2018
DOI : 10.1117/12.2293209
Copyright Disclaimer : © (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Uncontrolled Keywords : Deep learning; Model observer; Simulation; Lesion; Mammography; Virtual clinical trial
Related URLs :
Depositing User : Clive Harris
Date Deposited : 18 Sep 2018 07:12
Last Modified : 18 Sep 2018 07:39
URI: http://epubs.surrey.ac.uk/id/eprint/849307

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