Computer Vision News - May 2021

7 On B1, one can see in color an example of a retinal fundus image, whereas (B2, B3) show the same retinal image but in black and white. Machine learning predictions of diabetes and body mass index (BMI) are dependent on the features of the vasculature and optic disc, as indicated by the soft attention heat map with green colour in the images. OnC, the complex neural networks are towards the cortical, subcortical, and cerebellar areas and are involved in voluntary saccadic eye movements for attentional control. The red arrows indicate the direct pathway (PEF, the parietal eye fields; FEF, frontal eye field; SEF, supplementary eye field) to the superior colliculus (SC) and brainstem premotor regions, while yellow arrows indicate the indirect pathway to the SC and brainstem premotor regions via the basal ganglia (striatum, subthalamic nucleus, globus pallidus, and substantia nigra pars reticularis). On D, an architectural model of the hierarchy of visual cortical circuitry is displayed. The pathway is a feed-forward ascending starting from the retinas to the cortex, as well as a feedback descending pathway from the cortex to multiple downstream areas. Finally on E, there is a possible solution to develop an eye-brain engineering to compute human brain states, mainly based on smart cameras to detect ocular responses, combined with other biological signals including electroencephalography (EEG) and photoplethysmography (PPG). The table from the same paper presents the similarity of the features discovered between those diseases with the possibility to use some of them as biomarkers for the approach of machine learning. Computer Vision for Brain Disorders Based Primarily on Ocular Responses