Paper
28 March 2013 High throughput screening for mammography using a human-computer interface with rapid serial visual presentation (RSVP)
Chris Hope, Annette Sterr, Premkumar Elangovan, Nicholas Geades, David Windridge, Ken Young, Kevin Wells
Author Affiliations +
Abstract
The steady rise of the breast cancer screening population, coupled with data expansion produced by new digital screening technologies (tomosynthesis/CT) motivates the development of new, more efficient image screening processes. Rapid Serial Visual Presentation (RSVP) is a new fast-content recognition approach which uses electroencephalography to record brain activity elicited by fast bursts of image data. These brain responses are then subjected to machine classification methods to reveal the expert’s ‘reflex’ response to classify images according to their presence or absence of particular targets. The benefit of this method is that images can be presented at high temporal rates (~10 per second), faster than that required for fully conscious detection, facilitating a high throughput of image (screening) material. In the present paper we present the first application of RSVP to medical image data, and demonstrate how cortically coupled computer vision can be successfully applied to breast cancer screening. Whilst prior RSVP work has utilised multichannel approaches, we also present the first RSVP results demonstrating discriminatory response on a single electrode with a ROC area under the curve of 0.62- 0.86 using a simple Fisher discriminator for classification. This increases to 0.75 – 0.94 when multiple electrodes are used in combination.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chris Hope, Annette Sterr, Premkumar Elangovan, Nicholas Geades, David Windridge, Ken Young, and Kevin Wells "High throughput screening for mammography using a human-computer interface with rapid serial visual presentation (RSVP)", Proc. SPIE 8673, Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment, 867303 (28 March 2013); https://doi.org/10.1117/12.2007557
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CITATIONS
Cited by 9 scholarly publications and 2 patents.
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KEYWORDS
Mammography

Electroencephalography

Image segmentation

Visualization

Electrodes

Brain-machine interfaces

Image processing

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