Visual search, the process of detecting and identifying objects using eye movements (saccades) and foveal vision, has been studied for identification of root causes of errors in the interpretation of mammograms. The aim of this study is to model visual search behavior of radiologists and their interpretation of mammograms using deep machine learning approaches. Our model is based on a deep convolutional neural network, a biologically inspired multilayer perceptron that simulates the visual cortex and is reinforced with transfer learning techniques. Eye-tracking data were obtained from eight radiologists (of varying experience levels in reading mammograms) reviewing 120 two-view digital mammography cases (59 cancers), and it has been used to train the model, which was pretrained with the ImageNet dataset for transfer learning. Areas of the mammogram that received direct (foveally fixated), indirect (peripherally fixated), or no (never fixated) visual attention were extracted from radiologists' visual search maps (obtained by a head mounted eye-tracking device). These areas along with the radiologists' assessment (including confidence in the assessment) of the presence of suspected malignancy were used to model: (1) radiologists' decision, (2) radiologists' confidence in such decisions, and (3) the attentional level (i.e., foveal, peripheral, or none) in an area of the mammogram. Our results indicate high accuracy and low misclassification in modeling such behaviors.

Mammography has long been considered as the primary technique in breast cancer detection and assessment. Despite low specificity, mammography has been preferred over other contemporary techniques such as magnetic resonance imaging (MRI), computed tomography (CT) and ultrasonography (US) due to superior sensitivity and significant health economic benefits. The development of a new technique, a limited angle cone beam pseudo‐three‐dimensional tomosynthesis, digital breast tomosynthesis (DBT), has gained momentum. Several preliminary studies and ongoing trials are showing evidence of the benefits of DBT in improving lesion visibility, accuracy of cancer detection and observer performance. This raises the possibility of adoption of DBT in the breast cancer assessment clinic, wherein confirming or dismissing the presence of malignancy (at the potential site identified during screening) is of utmost importance. Identification of suspected malignancy in terms of lesion characteristics and location is also essential in assessment. In this literature review, we evaluate the role of DBT for use in breast cancer assessment and its future in biopsy.

• Use of DBT in the assessment clinic results in increased diagnostic accuracy. • Use of DBT in the assessment clinic improves performance of radiologists and also increases the confidence in their decisions. • DBT may reduce the need for additional views, ultrasound imaging, and biopsy.

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