Texture as a Diagnostic Signal in Mammograms

Yelda Semizer; Melchi Michel; Karla Evans; Jeremy Wolfe

Texture as a Diagnostic Signal in Mammograms

Yelda Semizer, Melchi Michel, Karla Evans, Jeremy Wolfe

Psychology

Research output: Contribution to conference › Other › peer-review

Abstract

Radiologists can discriminate between normal and abnormal
breast tissue at a glance, suggesting that radiologists might be
using some “global signal” of abnormality. Our study investigated
whether texture descriptions can be used to characterize
the global signal of abnormality and whether radiologists
use this information during interpretation. Synthetic images
were generated using a texture synthesis algorithm trained on
texture descriptions extracted from sections of mammograms.
Radiologists completed a task that required rating the abnormality
of briefly presented tissue sections. When the abnormal
tissue had no visible lesion, radiologists seemed to use texture
descriptions; performance was similar across real and synthesized
tissue sections. However, when the abnormal tissue had a
visible lesion, radiologists seemed to rely on additional mechanisms
beyond the texture descriptions; performance increased
for the real tissue sections. These findings suggest that radiologists
can use texture descriptions as global signals of abnormality
in interpretation of breast tissue.

Original language	English
Publication status	Accepted/In press - 2018

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cogsci2018_revised

Cite this

@conference{a99b991c79344ed1bb58e509a10a9460,

title = "Texture as a Diagnostic Signal in Mammograms",

abstract = "Radiologists can discriminate between normal and abnormalbreast tissue at a glance, suggesting that radiologists might beusing some “global signal” of abnormality. Our study investigatedwhether texture descriptions can be used to characterizethe global signal of abnormality and whether radiologistsuse this information during interpretation. Synthetic imageswere generated using a texture synthesis algorithm trained ontexture descriptions extracted from sections of mammograms.Radiologists completed a task that required rating the abnormalityof briefly presented tissue sections. When the abnormaltissue had no visible lesion, radiologists seemed to use texturedescriptions; performance was similar across real and synthesizedtissue sections. However, when the abnormal tissue had avisible lesion, radiologists seemed to rely on additional mechanismsbeyond the texture descriptions; performance increasedfor the real tissue sections. These findings suggest that radiologistscan use texture descriptions as global signals of abnormalityin interpretation of breast tissue.",

author = "Yelda Semizer and Melchi Michel and Karla Evans and Jeremy Wolfe",

year = "2018",

language = "English",

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TY - CONF

T1 - Texture as a Diagnostic Signal in Mammograms

AU - Semizer, Yelda

AU - Michel, Melchi

AU - Evans, Karla

AU - Wolfe, Jeremy

PY - 2018

Y1 - 2018

N2 - Radiologists can discriminate between normal and abnormalbreast tissue at a glance, suggesting that radiologists might beusing some “global signal” of abnormality. Our study investigatedwhether texture descriptions can be used to characterizethe global signal of abnormality and whether radiologistsuse this information during interpretation. Synthetic imageswere generated using a texture synthesis algorithm trained ontexture descriptions extracted from sections of mammograms.Radiologists completed a task that required rating the abnormalityof briefly presented tissue sections. When the abnormaltissue had no visible lesion, radiologists seemed to use texturedescriptions; performance was similar across real and synthesizedtissue sections. However, when the abnormal tissue had avisible lesion, radiologists seemed to rely on additional mechanismsbeyond the texture descriptions; performance increasedfor the real tissue sections. These findings suggest that radiologistscan use texture descriptions as global signals of abnormalityin interpretation of breast tissue.

AB - Radiologists can discriminate between normal and abnormalbreast tissue at a glance, suggesting that radiologists might beusing some “global signal” of abnormality. Our study investigatedwhether texture descriptions can be used to characterizethe global signal of abnormality and whether radiologistsuse this information during interpretation. Synthetic imageswere generated using a texture synthesis algorithm trained ontexture descriptions extracted from sections of mammograms.Radiologists completed a task that required rating the abnormalityof briefly presented tissue sections. When the abnormaltissue had no visible lesion, radiologists seemed to use texturedescriptions; performance was similar across real and synthesizedtissue sections. However, when the abnormal tissue had avisible lesion, radiologists seemed to rely on additional mechanismsbeyond the texture descriptions; performance increasedfor the real tissue sections. These findings suggest that radiologistscan use texture descriptions as global signals of abnormalityin interpretation of breast tissue.

M3 - Other

ER -