Optical Flow, Positioning, and Eye Coordination: Automating the Annotation of PhysicianPatient Interactions The widespread adoption of electronic health records within clinical settings has renewed interest in understanding physicianpatient interactions. Previous work analyzing clinical interactions has mostly coupled patient surveys with manually annotated video interactions provided by human coders. Physician gaze is among the components of the nonverbal interaction which has been found to impact patient outcomes. The work described in this paper illustrates an automated system for multivideo labeling of patientphysician interactions and shows that image features (in the form of body positioning coordinates and optical flow) can provide important visual aids for learning physician gaze with over 90% accuracy. While our approach focuses on physician gaze, it can be extended to capture other clinical humanhuman and humantechnology interactions as well as connect these interactions to patient ratings of clinical interactions.

MedGaze: Gaze Estimation on WCE Images Based on a CNN Autoencoder The interpretation of medical images depends on physiciansu0027 experience. Over time, physicians develop their ability to examine the images, and this is usually reflected on gaze patterns they follow to observe visual cues, which lead them to diagnostic decisions. In the context of gaze prediction, graph and machine learning methods have been proposed for the visual saliency estimation on generic images. In this work we preset a novel and robust gaze estimation methodology based on physiciansu0027 eye fixations, using convolutional neural networks combined with regularization methods, on medical images taken during Wireless Capsule Endoscopy (WCE). Furthermore, we present a novel dataset of physiciansu0027 eye fixation patterns which was used for the training of the neural network model. The model was able to achieve 68.5% Juddu0027s Area Under the receiver operating Characteristic (AUCJ).