Abstract: Methods and systems for generating a prediction value of a Neural Network (NN). The method is executable by a processor and comprises generating, by the processor employing a feature extraction sub-network, a plurality of features based on an input object, generating, by the processor employing a detection sub-network, a detection output based on the plurality of features, the detection sub-network having been trained to generate the detection output indicative of a human-interpretable output for a given portion of the input object; generating, by the processor employing a prediction sub-network, the prediction value based on the human-interpretable output and the given portion of the input object; and providing, by the processor, an indication of the prediction value and the human-interpretable output via a user interface.

Abstract: Methods and systems for generating a semantically augmented image are disclosed. An embedding is generated for each object label associated with a segmented input image. For each embedding associated with a respective object label, a similarity score is computed between the embedding associated with the object label and an embedding representing an object class in an object bank storing a plurality of object images. At least one object is selected, the selected object being associated with a respective object image in the object bank, the selected at least one object being from an identified object class that is identified as contextually relevant to at least one object label associated with the segmented input image, based at least on the similarity score. The selected object is added into the segmented input image to generate the augmented image.

Abstract: Methods and systems for generating a semantically augmented image are disclosed. An embedding is generated for each object label associated with a segmented input image. For each embedding associated with a respective object label, a similarity score is computed between the embedding associated with the object label and an embedding representing an object class in an object bank storing a plurality of object images. At least one object is selected, the selected object being associated with a respective object image in the object bank, the selected at least one object being from an identified object class that is identified as contextually relevant to at least one object label associated with the segmented input image, based at least on the similarity score. The selected object is added into the segmented input image to generate the augmented image.

Abstract: Optical coherence tomography (OCT) may be used to acquire cross-sectional or volumetric images of any specimen, including biological specimens such as the retina. Additional processing of the OCT data may be performed to generate images of features of interest. In some embodiments, these features may be in motion relative to their surroundings, e.g., blood in the retinal vasculature. In some embodiments, an acquired image may be degraded by motion artifact. The proposed invention describes OCT system embodiments that may be configured for multi-scale imaging, with the capability of switching between low or high lateral resolution, and with the assistance of adaptive optics for aberration correction. The invention also describes a method for enhancing OCT image quality by reducing or eliminating the negative effects introduced by sources and speed by performing bidirectional scanning.

Abstract: Optical coherence tomography (OCT) may be used to acquire cross-sectional or volumetric images of any specimen, including biological specimens such as the retina. Additional processing of the OCT data may be performed to generate images of features of interest. In some embodiments, these features may be in motion relative to their surroundings, e.g., blood in the retinal vasculature. The proposed invention describes a combination of images acquired by OCT, manual segmentations of these images by experts, and an artificial neural network for the automated segmentation and classification of features in the OCT images. As a specific example, the performance of the systems and methods described herein are presented for the automatic segmentation of blood vessels in images acquired with OCT angiography.