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Title: Breast screening: visual search as an aid for digital mammographic interpretation training
Authors: Chen, Yan
Turnbull, Ann
James, Jonathan
Gale, Alastair G.
Scott, Hazel J.
Keywords: Mammographic interpretation training
Low resolution devices
Eye movements
Issue Date: 2010
Publisher: © 2010 Society of Photo-Optical Instrumentation Engineers
Citation: CHEN, Y. ... et al., 2010. Breast screening: visual search as an aid for digital mammographic interpretation training. IN: Medical Imaging 2010: Image Perception, Observer Performance, and Technology Assessment, edited by David J. Manning, Craig K. Abbey, Proc. SPIE 7627,76270C (2010).
Abstract: Digital mammography is gradually being introduced across all breast screening centres in the UK during 2010. This provides increased training opportunities using lower resolution, lower cost and more widely available devices, in addition to the clinical digital mammography workstations. This study examined how experienced breast screening personnel performed when they examined sets of difficult DICOM two-view screening cases in three conditions: on GE digital mammography workstations, on a standard LCD monitor (using a DICOM viewer) and an iPhone (running Osirix software). In each condition they either viewed the full images unaided or were permitted to use the post-processing manipulations of pan, zoom and window level/width adjustments. For each case they had to report the feature type, rate their confidence on the presence of abnormality, classify the case and specify case density. Their visual search behaviour was recorded throughout using a head mounted eye tracker. Additionally aspects of their real life screening performance and performance on a national self assessment scheme were examined. Data indicate that screening experience plays a major role in doing well on the self assessment scheme. Task performance was best on the clinical workstation. However, the data also suggest that a DICOM viewer that runs on a PC or laptop with a standard LCD display allows viewing digital images in full resolution support impressive cancer detection performance. The iPhone is not ideal for examining full images due to the amount of scrolling and zooming required. Overall, the results indicate that low cost devices could be used to provide additional tailored training as long as device resolution and HCI aspects are carefully considered.
Description: Copyright 2010 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. This paper can also be found at: http://dx.doi.org/10.1117/12.843820
Version: Published
DOI: 10.1117/12.843820
URI: https://dspace.lboro.ac.uk/2134/6286
ISBN: 9780819480286
ISSN: 1605-7422
Appears in Collections:Conference Papers and Presentations (Computer Science)

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