Abstract: Methods, systems, and computer-readable media are disclosed herein for automated identification and alerting of neurodegenerative diseases (NDD). Patient information comprising medical history information is accessed. Using the patient information, one or more risk factors and/or symptoms for a NDD are identified. Using the identified risk factors and/or symptoms, a patient with probable risk for developing the NDD is determined. A notification of the probable risk that the patient develops the NDD is provided.

Abstract: Estimating 3D human pose from monocular images is a challenging problem due to the variety and complexity of human poses and the inherent ambiguity in recovering depth from single view. Recent deep learning based methods show promising results by using supervised learning on 3D pose annotated datasets. However, the lack of large-scale 3D annotated training data makes the 3D pose estimation difficult in-the-wild. Embodiments of the present disclosure provide a method which can effectively predict 3D human poses from only 2D pose in a weakly-supervised manner by using both ground-truth 3D pose and ground-truth 2D pose based on re-projection error minimization as a constraint to predict the 3D joint locations. The method may further utilize additional geometric constraints on reconstructed body parts to regularize the pose in 3D along with minimizing re-projection error to improvise on estimating an accurate 3D pose.

Abstract: This disclosure relates to systems and methods for solving generic inverse problems by providing a coupled representation architecture using transform learning. Convention solutions are complex, require long training and testing times, reconstruction quality also may not be suitable for all applications. Furthermore, they preclude application to real-time scenarios due to the mentioned inherent lacunae. The methods provided herein require involve very low computational complexity with a need for only three matrix-vector products, and requires very short training and testing times, which makes it applicable for real-time applications. Unlike the conventional learning architectures using inductive approaches, the CASC of the present disclosure can learn directly from the source domain and the number of features in a source domain may not be necessarily equal to the number of features in a target domain.

Abstract: Aspects of the present disclosure determine a risk level of Polycystic Ovarian syndrome (“PCOS”). Alongside the risk level of PCOS, aspects of the present disclosure simultaneously display the risk level of PCOS with patient information, which may be useful for clinicians. Aspects include receiving patient information of a patient, determining a patient criteria is satisfied based on the patient information, and applying a predictive diagnosis model to the patient information to determine a risk level of PCOS.

Abstract: Computerized systems and methods are provided for determining the risk of developing gestation diabetes mellitus (GDM) and assigning workflows based on such a determination. The systems and methods can include receiving medical information associated with an individual, determining whether the individual is at risk of developing GDM based on the received medical information, and performing one or more response actions. The one or more response actions can include assigning a workflow for preventative treatment of GDM, providing a notification that the individual is at risk of GDM, or a combination thereof.

Abstract: Estimating 3D human pose from monocular images is a challenging problem due to the variety and complexity of human poses and the inherent ambiguity in recovering depth from single view. Recent deep learning based methods show promising results by using supervised learning on 3D pose annotated datasets. However, the lack of large-scale 3D annotated training data makes the 3D pose estimation difficult in-the-wild. Embodiments of the present disclosure provide a method which can effectively predict 3D human poses from only 2D pose in a weakly-supervised manner by using both ground-truth 3D pose and ground-truth 2D pose based on re-projection error minimization as a constraint to predict the 3D joint locations. The method may further utilize additional geometric constraints on reconstructed body parts to regularize the pose in 3D along with minimizing re-projection error to improvise on estimating an accurate 3D pose.

Abstract: This disclosure relates to systems and methods for solving generic inverse problems by providing a coupled representation architecture using transform learning. Convention solutions are complex, require long training and testing times, reconstruction quality also may not be suitable for all applications. Furthermore, they preclude application to real-time scenarios due to the mentioned inherent lacunae. The methods provided herein require involve very low computational complexity with a need for only three matrix-vector products, and requires very short training and testing times, which makes it applicable for real-time applications. Unlike the conventional learning architectures using inductive approaches, the CASC of the present disclosure can learn directly from the source domain and the number of features in a source domain may not be necessarily equal to the number of features in a target domain.

Abstract: Motion blur occur when acquiring images and videos with cameras fitted to the high speed motion devices, for example, drones. Distorted images intervene with the mapping of the visual points, hence the pose estimation and tracking may get corrupted. System and method for solving inverse problems using a coupled autoencoder is disclosed. In an embodiment, solving inverse problems, for example, generating a clean sample from an unknown corrupted sample is disclosed. The coupled autoencoder learns the autoencoder weights and coupling map (between source and target) simultaneously. The technique is applicable to any transfer learning problem. The embodiments of the present disclosure implements/proposes a new formulation that recasts deblurring as a transfer learning problem which is solved using the proposed coupled autoencoder.

Abstract: Motion blur occur when acquiring images and videos with cameras fitted to the high speed motion devices, for example, drones. Distorted images intervene with the mapping of the visual points, hence the pose estimation and tracking may get corrupted. System and method for solving inverse problems using a coupled autoencoder is disclosed. In an embodiment, solving inverse problems, for example, generating a clean sample from an unknown corrupted sample is disclosed. The coupled autoencoder learns the autoencoder weights and coupling map (between source and target) simultaneously. The technique is applicable to any transfer learning problem. The embodiments of the present disclosure implements/proposes a new formulation that recasts deblurring as a transfer learning problem which is solved using the proposed coupled autoencoder.