Abstract: Various methods and systems are provided for automatically registering and stitching images. In one example, a method includes entering a first image of a subject and a second image of the subject to a model trained to output a transformation matrix based on the first image and the second image, where the model is trained with a plurality of training data sets, each training data set including a pair of images, a mask indicating a region of interest (ROI), and associated ground truth, automatically stitching together the first image and the second image based on the transformation matrix to form a stitched image, and outputting the stitched image for display on a display device and/or storing the stitched image in memory.

Abstract: Systems/techniques that facilitate AI-based region-of-interest masks for improved data reconstructions are provided. In various embodiments, a system can access a set of two-dimensional medical scan projections. In various aspects, the system can generate a set of two-dimensional region-of-interest masks respectively corresponding to the set of two-dimensional medical scan projections. In various instances, the system can generate a region-of-interest visualization based on the set of two-dimensional region-of-interest masks and the set of two-dimensional medical scan projections. In various cases, the system can generate the set of two-dimensional region-of-interest masks by executing a machine learning segmentation model on the set of two-dimensional medical scan projections.

Abstract: Techniques are described for generating multimodal training data cohorts tailored to specific clinical machine learning (ML) model inferencing tasks. In an embodiment, a method comprises accessing, by a system comprising a processor, multimodal clinical data for a plurality of subjects included in one or more clinical data sources. The method further comprises selecting, by the system, datasets from the multimodal clinical data based on the datasets respectively comprising subsets of the multimodal clinical data that satisfy criteria determined to be relevant to a clinical processing task. The method further comprises generating, by the system, a training data cohort comprising the datasets for training a clinical inferencing model to perform the clinical processing task.

Abstract: Systems and techniques that facilitate freeze-out as a regularizer in training neural networks are presented. A system can include a memory and a processor that executes computer executable components. The computer executable components can include: an assessment component that identifies units of a neural network, a selection component that selects a subset of units of the neural network, and a freeze-out component that freezes the selected subset of units of the neural network so that weights of output connections from the frozen subset of units will not be updated for a training run.

Abstract: Cosmeceutical compositions for and methods of improving the appearance of skin and promoting hair growth are provided. Said compositions comprise at least one ROS-scavenging polymer gel, at least MG53, or a combination thereof. A novel boronate ester-based copolymer forms an aqueous hydrogel exhibiting lower viscosity at cooler temperatures and higher viscosity at higher temperatures is provided.

Abstract: Methods and systems are provided for inferring thickness and volume of one or more object classes of interest in two-dimensional (2D) medical images, using deep neural networks. In an exemplary embodiment, a thickness of an object class of interest may be inferred by acquiring a 2D medical image, extracting features from the 2D medical image, mapping the features to a segmentation mask for an object class of interest using a first convolutional neural network (CNN), mapping the features to a thickness mask for the object class of interest using a second CNN, wherein the thickness mask indicates a thickness of the object class of interest at each pixel of a plurality of pixels of the 2D medical image; and determining a volume of the object class of interest based on the thickness mask and the segmentation mask.

Abstract: Compositions for and methods of improving tissue function are provided. Said compositions comprise MG53 or express MG53. Said compositions can be used for improving the function of non-diseased and uninjured tissue in subjects.

Abstract: Compositions for and methods of treating liver injury are provided. Said compositions comprise MG53 or express MG53. Said compositions can be used for treating chronic or acute injured liver tissue and can be administered systemically, locally, or both.

Abstract: Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.

Abstract: Systems and techniques that facilitate synthetic training data generation for improved machine learning generalizability are provided. In various embodiments, an element augmentation component can generate a set of preliminary annotated training images based on an annotated source image. In various aspects, a preliminary annotated training image can be formed by inserting at least one element of interest or at least one background element into the annotated source image. In various instances, a modality augmentation component can generate a set of intermediate annotated training images based on the set of preliminary annotated training images. In various cases, an intermediate annotated training image can be formed by varying at least one modality-based characteristic of a preliminary annotated training image. In various aspects, a geometry augmentation component can generate a set of deployable annotated training images based on the set of intermediate annotated training images.

Abstract: Compositions for and methods of preventing, reversing or treating viral infection-induced organ failure provided. The compositions are also suitable for treating and/or preventing COVID-19 and influenza. The compositions and methods employ MG53, which can be in the form of recombinant human MG53. The MG53 may also be administered as a composition that expresses and releases MG53 after in vivo administration of said composition to a subject.

Abstract: Techniques are described for optimizing deep learning model performance using image harmonization as a pre-processing step. According to an embodiment, a method comprises decomposing, by a system operatively coupled to a processor, an input image into sub-images. The method further comprises harmonizing the sub-images with corresponding reference sub-images of at least one reference image based on two or more different statistical values respectively calculated for the sub-images and the corresponding reference-sub images, resulting in transformation of the sub-images into modified sub-images images. In some implementations, the modified sub-images can be combined into a harmonized image having a more similar appearance to the at least one reference image relative to the input image.

Abstract: Compositions for and methods of preventing, reversing or treating viral infection-induced organ failure provided. The compositions are also suitable for treating and/or preventing COVID-19 and influenza. The compositions and methods employ MG53, which can be in the form of recombinant human MG53. The MG53 may also be administered as a composition that expresses and releases MG53 after in vivo administration of said composition to a subject.

Abstract: Systems and techniques that facilitate freeze-out as a regularizer in training neural networks are presented. A system can include a memory and a processor that executes computer executable components. The computer executable components can include: an assessment component that identifies units of a neural network, a selection component that selects a subset of units of the neural network, and a freeze-out component that freezes the selected subset of units of the neural network so that weights of output connections from the frozen subset of units will not be updated for a training run.

Abstract: Compositions for and methods of treating corneal injury, among other tissue of or around the eye, are provided. Said compositions comprise MG53 or express MG53. Said compositions can be used for treating chronic or acute injured tissue of the eye or orbit of the eye and can be administered systemically, locally, or both.

Abstract: A multimedia broadcasting system having a multiple-node structure includes nodes. Each node is coupled to at least one of the nodes, and the nodes include server nodes and multimedia-playing terminal nodes. Each server node is coupled to at least one of the server nodes and provides at least one multimedia content. Each multimedia-playing terminal node receives multimedia content transmitted by a server node of the server nodes and plays the multimedia content. A first server node of the server nodes is coupled to a second server node of the server nodes, and the first server node transmits, via the second server node, a first multimedia content to at least one multimedia-playing terminal node controlled by the second server node. Each multimedia-playing terminal node is a multimedia play terminal including at least one display device or audio playing device.

Abstract: The present disclosure discloses a fast switching method, device and terminal of a dual system, the method includes: creating a short-cut icon of an application of a second system according to an input creation instruction, and displaying the short-cut icon in a first system; and switching to the second system and running the application corresponding to the short-cut icon when the short-cut icon is triggered. The present disclosure can directly start the application of the second system by means of the short-cut icon, switching operations between the dual system are simplified, and it is convenient for the user to use.

Abstract: A power circuit, a converter structure and a wind power generation system thereof are disclosed. The power circuit includes a first converter having an AC input side and a DC output side, a second converter having a DC input side and an AC output side, and a DC bus storage unit electrically connected to the DC output side of the first converter and the DC input side of the second converter. A level number, a switching valve type and/or a circuit connection of the first converter are different from those of the second converter.

Abstract: A multimedia broadcasting system having a multiple-node structure includes nodes. Each node is coupled to at least one of the nodes, and the nodes include server nodes and multimedia-playing terminal nodes. Each server node is coupled to at least one of the server nodes and provides at least one multimedia content. Each multimedia-playing terminal node receives multimedia content transmitted by a server node of the server nodes and plays the multimedia content. A first server node of the server nodes is coupled to a second server node of the server nodes, and the first server node transmits, via the second server node, a first multimedia content to at least one multimedia-playing terminal node controlled by the second server node. Each multimedia-playing terminal node is a multimedia play terminal including at least one display device or audio playing device.