Modeling visual search behavior of breast radiologists using a deep convolution neural network

Abstract

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.

Tomographic Ultrasound Imaging in the Diagnosis of Breast Tumors under the Guidance of Deep Learning Algorithms

“…e results showed that the light scattering imaging results were compared with the pathological results, and the diagnosis coincidence rate was 81.4%, which was consistent with the literature report [17]. At present, deep learning technology has been widely used in various fields, such as military, medicine, and geography [18,19]. Due to the continuous development of imaging technology, a large amount of imaging data has been obtained clinically, but how to mine useful information from the massive imaging data and apply it to the diagnosis of clinical diseases is one of the goals that need to be achieved at present.…”

Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems

“…Stember [47] utilized eye tracking with deep learning for generating segmentation masks based on eye tracking data and achieved results that were similar to hand annotations. Similarly, Aresta [48] and Mall [49] utilized gaze information for automatic lung nodule detection and mammogram interpretations. Hermanson [50] conducted a study to identify the visual search patters of dentists while viewing and interpreting periapical dental radiographs.…”

Development of Efficient Classification Systems for the Diagnosis of Melanoma

“…Mall et al [8] have used mammograms to analyze the proximity of women's breast cancer cells. In the wake of different mammograms gathered from various radiographs, radiology assessments were similarly taken and contributed to CNN's preparation.…”