Gist Perception and Holistic Processing in Rapidly Presented Mammograms.

Chin, Michael D.; Evans, Karla K.; Wolfe, Jeremy M.; Tanaka, James

doi:10.1167/18.10.391

Cited by 2 publications

(2 citation statements)

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“…To collect this signal, we used an in-house developed MATLAB application to view images only for 500 ms. Previous studies suggested that 500 ms are enough to capture the gist signal while ensuring that the radiologists were not able to process any area with foveal vision 3 , 9 . The image presentation protocol to collect the gist signal is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…The Kundel et al “holistic processing model” assumes a gist signal with a localized source 2 . Alternatively, recent studies 3 – 5 suggest another type of gist signal, which arises from global image properties and does not rely on the presence of a localizable lesion. It is hypothesized that such a signal is associated with the textural changes in the breasts of women at higher risk of current or future breast cancer 6 .…”

Section: Introductionmentioning

confidence: 99%

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Global processing provides malignancy evidence complementary to the information captured by humans or machines following detailed mammogram inspection

Gandomkar

Siviengphanom

Ekpo

et al. 2021

Sci Rep

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The information captured by the gist signal, which refers to radiologists’ first impression arising from an initial global image processing, is poorly understood. We examined whether the gist signal can provide complementary information to data captured by radiologists (experiment 1), or computer algorithms (experiment 2) based on detailed mammogram inspection. In the first experiment, 19 radiologists assessed a case set twice, once based on a half-second image presentation (i.e., gist signal) and once in the usual viewing condition. Their performances in two viewing conditions were compared using repeated measure correlation (rm-corr). The cancer cases (19 cases × 19 readers) exhibited non-significant trend with rm-corr = 0.012 (p = 0.82, CI: −0.09, 0.12). For normal cases (41 cases × 19 readers), a weak correlation of rm-corr = 0.238 (p < 0.001, CI: 0.17, 0.30) was found. In the second experiment, we combined the abnormality score from a state-of-the-art deep learning-based tool (DL) with the radiological gist signal using a support vector machine (SVM). To obtain the gist signal, 53 radiologists assessed images based on half-second image presentation. The SVM performance for each radiologist and an average reader, whose gist responses were the mean abnormality scores given by all 53 readers to each image was assessed using leave-one-out cross-validation. For the average reader, the AUC for gist, DL, and the SVM, were 0.76 (CI: 0.62–0.86), 0.79 (CI: 0.63–0.89), and 0.88 (CI: 0.79–0.94). For all readers with a gist AUC significantly better than chance-level, the SVM outperformed DL. The gist signal provided malignancy evidence with no or weak associations with the information captured by humans in normal radiologic reporting, which involves detailed mammogram inspection. Adding gist signal to a state-of-the-art deep learning-based tool improved its performance for the breast cancer detection.

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Section: Methodsmentioning

confidence: 99%