A two-stage detection model applied to skilled visual search by radiologists

Swensson, Richard G.

doi:10.3758/bf03199899

Cited by 60 publications

(53 citation statements)

References 11 publications

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“…Instead, there appears to be a set of one or two dozen that will serve this role (26). In medical image perception, the concept of Swensson (27) of a two-stage detection model was a precursor to ideas about feature guidance. Briefly, this model postulates two serial processing stages: an initial stage of global processing that functions as an attentional filter, followed by a second stage in which explicit attention is focused on those areas that were tagged as interesting in the first stage.…”

Section: Forms Of Guidance In Visual Searchmentioning

confidence: 99%

Informatics in Radiology: What Can You See in a Single Glance and How Might This Guide Visual Search in Medical Images?

et al. 2013

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Diagnostic accuracy for radiologists is above that expected by chance when they are exposed to a chest radiograph for only one-fifth of a second, a period too brief for more than a single voluntary eye movement. How do radiologists glean information from a first glance at an image? It is thought that this expert impression of the gestalt of an image is related to the everyday, immediate visual understanding of the gist of a scene. Several high-speed mechanisms guide our search of complex images. Guidance by basic features (such as color) requires no learning, whereas guidance by complex scene properties is learned. It is probable that both hardwired guidance by basic features and learned guidance by scene structure become part of radiologists' expertise. Search in scenes may be best explained by a two-pathway model: Object recognition is performed via a selective pathway in which candidate targets must be individually selected for recognition. A second, nonselective pathway extracts information from global or statistical information without selecting specific objects. An appreciation of the role of nonselective processing may be particularly useful for understanding what separates novice from expert radiologists and could help establish new methods of physician training based on medical image perception.

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Section: Forms Of Guidance In Visual Searchmentioning

confidence: 99%

Informatics in Radiology: What Can You See in a Single Glance and How Might This Guide Visual Search in Medical Images?

et al. 2013

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“…Also, for some disorders, the distinction between a feature and a diagnostic category is fuzzy at best. In radiology, for example, pulmonary nodules (see, e.g., Swensson, 1980) or bowel polyps (Markus, Somers, O'Malley, & Stephenson, 1989) are discussed as features, but are themselves disorders that are candidates for direct treatment.…”

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confidence: 99%

The correlation of feature identification and category judgments in diagnostic radiology

et al. 1992

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“…It has been argued for many years that an initial, global processing step is an important component in expert medical image perception that might constrain or filter subsequent search (12)(13)(14)(15), with the two most prominent models (16,17) each placing great emphasis on experts' ability to process and evaluate information from large regions of an image (18). These models are broadly consistent with two-stage models of visual search (19,20), developed in the basic vision literature that propose that there is a limited set of features that can be used to guide attention and subsequent serial stage that allows for "binding" of features to permit identification of objects.…”

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confidence: 99%

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

Evans

Haygood

Cooper

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

137

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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.gist processing | medical image perception | attention | mammography R apid extraction of scene "gist" (1-4) is a very useful aspect of routine visual perception that allows us to allocate our time and attention intelligently when confronted with new visual information (Can I find food here? Is there danger here?). The signals that we extract on our first glimpse of a scene are imperfect but not random. Experts often anecdotally report gistlike experiences with complex images in their domain of expertise. For instance, we have shown that radiologists can distinguish normal from abnormal mammograms at above-chance levels in as little as a quarter of a second, whereas nonexperts cannot (5). The gist of abnormality appears to be a global signal. Radiologists can detect it but cannot even crudely localize the abnormality under these conditions.Detecting the gist of breast cancer might be more than a curiosity, if that signal could be used to improve performance in breast cancer screening. Screening mammography can reduce mortality through early diagnosis of disease (6). Breast cancer is the most prevalent cancer in women and is the second leading cause of cancer deaths in women (7). In North America, screening mammography has a false negative rate of 20 to 30% (8, 9) and a recall rate of about 10% (10). With a disease prevalence of about 0.3% (11), the vast majority of those recalled will not have cancer. Thus, there is significant room for improvement.It has been argued for many years that an initial, global processing step is an i...

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A two-stage detection model applied to skilled visual search by radiologists

Cited by 60 publications

References 11 publications

Informatics in Radiology: What Can You See in a Single Glance and How Might This Guide Visual Search in Medical Images?

Informatics in Radiology: What Can You See in a Single Glance and How Might This Guide Visual Search in Medical Images?

The correlation of feature identification and category judgments in diagnostic radiology

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

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