A half-second glimpse often lets radiologists identify breast cancer cases even when viewing the mammogram of the opposite breast

Karla K. Evans, Tamara Miner Haygood, Julie Cooper, Anne Marie Culpan, Jeremy M. Wolfe

Research output: Contribution to journalArticlepeer-review

71 Scopus citations

Abstract

Humans are very adept at extracting the "gist" of a scene in a fraction of a second. We have found that radiologists can discriminate normal from abnormal mammograms at above-chance levels after a half-second viewing (d? ∼ 1) but are at chance in localizing the abnormality. This pattern of results suggests that they are detecting a global signal of abnormality. What are the stimulus properties that might support this ability? We investigated the nature of the gist signal in four experiments by asking radiologists to make detection and localization responses about briefly presented mammograms in which the spatial frequency, symmetry, and/or size of the images was manipulated. We show that the signal is stronger in the higher spatial frequencies. Performance does not depend on detection of breaks in the normal symmetry of left and right breasts. Moreover, above-chance classification is possible using images from the normal breast of a patient with overt signs of cancer only in the other breast. Some signal is present in the portions of the parenchyma (breast tissue) that do not contain a lesion or that are in the contralateral breast. This signal does not appear to be a simple assessment of breast density but rather the detection of the abnormal gist may be based on a widely distributed image statistic, learned by experts. The finding that a global signal, related to disease, can be detected in parenchyma that does not contain a lesion has implications for improving breast cancer detection.

Original languageEnglish (US)
Pages (from-to)10292-10297
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume113
Issue number37
DOIs
StatePublished - Sep 13 2016
Externally publishedYes

Keywords

  • Attention
  • Gist processing
  • Mammography
  • Medical image perception

ASJC Scopus subject areas

  • General

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