We describe a novel approach for screening retinal imagery to detect evidence of abnormalities. In this paper, we focus our efforts on age-related macular degeneration (AMD), a pathology that may often go undetected in the early or intermediate stages, and can lead to a neovascular form often resulting in blindness, if untreated. Our strategy for retinal anomaly detection is to employ a single class classifier applied to fundus imagery. We use a multiresolution locally-adaptive scheme that identifies both normal and anomalous regions within the retina. We do this by using a hybrid parametric/non-parametric characterization of the support of the probability distribution of normal retinal tissue in color and intensity feature space. We apply this approach to screen for evidence of AMD on a dataset of 66 healthy and pathological cases and found a detection sensitivity and specificity of 95% and 96%.

Fast, collision-free motion through unknown environments remains a challenging problem for robotic systems. In these situations, the robot's ability to reason about its future motion is often severely limited by sensor field of view (FOV). By contrast, biological systems routinely make decisions by taking into consideration what might exist beyond their FOV based on prior experience. In this paper, we present an approach for predicting occupancy map representations of sensor data for future robot motions using deep neural networks. We evaluate several deep network architectures, including purely generative and adversarial models. Testing on both simulated and real environments we demonstrated performance both qualitatively and quantitatively, with SSIM similarity measure up to 0.899. We showed that it is possible to make predictions about occupied space beyond the physical robot's FOV from simulated training data. In the future, this method will allow robots to navigate through unknown environments in a faster, safer manner.

In an extension of previous work, 1,2 we assessed 2 deep learning (DL) methods addressing a 2-class age-related macular degeneration (AMD) referability classification: referable for the intermediate or advanced stage of AMD or not referable. Methods | We used 67 401 color fundus images (keeping only 1 image for each original stereo pair) from the National Eye Institute Age-Related Eye Disease Study (AREDS) data set 3 that were taken from 4613 individuals (who provided written consent) over a 12-year study, including baseline and follow-up visits, from November 13, 1992, to November 30, 2005. 1,2 The original AREDS image grading from certified graders at a fundus photograph reading center 3 were used as the gold standard. The present analysis was performed from January 22, 2018, through April 19, 2018. Use of the AREDS data set was performed following Johns Hopkins University School of Medicine Institutional Review Board approval. 2-and 4-Step Scales. This study addressed a standard 2-class classification problem-referable or nonreferable AMD-that was based on the original AREDS 4-step scale; details and criteria for the scale are described in the Box. Grades 3 and 4 in the 4-step scale correspond to higher risk for progression to advanced AMD.