Preliminary Study on Visual Attention Maps of Experts and Nonexperts When Examining Pathological Microscopic Images Pathological microscopic image is regarded as a gold standard for the diagnosis of disease, and eye tracking technology is considered as a very effective tool for medical education. It will be very interesting if we use the eye tracking to predict where pathologists or doctors and persons with no or little experience look at the pathological microscopic image. In the current work, we first establish a pathological microscopic image database with the eye movement data of experts and nonexperts (PMIDE), including a total of 425 pathological microscopic images. The statistical analysis is afterwards conducted on PMIDE to analyze the difference in eye movement behavior between experts and nonexperts. The results show that although there is no significant difference in general, the experts focus on a broader scope than nonexperts. This inspires us to respectively develop saliency models for experts and nonexperts. Furthermore, the existing 10 saliency models are tested on PMIDE, and the performance of these models are all unacceptable with AUC, CC, NSS and SAUC below 0.73, 0.47, 0.78 and 0.52, respectively. This study indicates that the saliency models specific to pathological microscopic images urgent need to be developed using our databasexe2x80x94PMIDE or the other related databases.

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).