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There are at least two categories of errors in radiology screening that canlead to suboptimal diagnostic decisions and interventions:(i)human fallibilityand (ii)complexity of visual search. Computer aided diagnostic (CAD) tools aredeveloped to help radiologists to compensate for some of these errors. However,despite their significant improvements over conventional screening strategies,most CAD systems do not go beyond their use as second opinion tools due toproducing a high number of false positives, which human interpreters need tocorrect. In parallel with efforts in computerized analysis of radiology scans,several researchers have examined behaviors of radiologists while screeningmedical images to better understand how and why they miss tumors, how theyinteract with the information in an image, and how they search for unknownpathology in the images. Eye-tracking tools have been instrumental in exploringanswers to these fundamental questions. In this paper, we aim to develop aparadigm shift CAD system, called collaborative CAD (C-CAD), that unifies bothof the above mentioned research lines: CAD and eye-tracking. We design aneye-tracking interface providing radiologists with a real radiology readingroom experience. Then, we propose a novel algorithm that unifies eye-trackingdata and a CAD system. Specifically, we present a new graph based clusteringand sparsification algorithm to transform eye-tracking data (gaze) into asignal model to interpret gaze patterns quantitatively and qualitatively. Theproposed C-CAD collaborates with radiologists via eye-tracking technology andhelps them to improve diagnostic decisions. The C-CAD learns radiologists'search efficiency by processing their gaze patterns. To do this, the C-CAD usesa deep learning algorithm in a newly designed multi-task learning platform tosegment and diagnose cancers simultaneously.

A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning