Abstract: A computer-implemented method for access path optimization is provided according to embodiments of the present disclosure. In the method, a plurality of real values of an access path factor may be collected during a specified time period. One of the real values may be generated when a query is executed on a first access path. Then, at least one second access path may be generated for the query based on the plurality of real values of the access path factor. Moreover, an optimal access path for the query may be identified from the first access path and the at least one second access path.

Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with estimating the motion of an anatomical structure. The motion estimation may be performed utilizing pre-learned knowledge of the anatomy of the anatomical structure. The anatomical knowledge may be learned via a variational autoencoder, which may then be used to optimize the parameters of a motion estimation neural network system such that, when performing motion estimation for the anatomical structure, the motion estimation neural network system may produce results that conform with the underlying anatomy of anatomical structure.

Abstract: Techniques, systems, and devices are described for providing a computational frame for estimating high-dimensional stochastic behaviors. In one exemplary aspect, a method for performing numerical estimation includes receiving a set of measurements of a stochastic behavior. The set of correlated measurements follows a non-standard probability distribution and is non-linearly correlated. Also, a non-linear relationship exists between a set of system variables that describes the stochastic behavior and a corresponding set of measurements. The method includes determining, based on the set of measurements, a numerical model of the stochastic behavior. The numerical model comprises a feature space comprising non-correlated features corresponding to the stochastic behavior. The non-correlated features have a dimensionality of M and the set of measurements has a dimensionality of N, M being smaller than N.

Abstract: Described herein are systems, methods, and instrumentalities associated with image segmentation such as tubular structure segmentation. An artificial neural network is trained to segment tubular structures of interest in a medical scan image based on annotated images of a different type of tubular structures that may have a different contrast and/or appearance from the tubular structures of interest. The training may be conducted in multiple stages during which a segmentation model learned from the annotated images during a first stage may be modified to fit the tubular structures of interest in a second stage. In examples, the tubular structures of interest may include coronary arteries, catheters, guide wires, etc., and the annotated images used for training the artificial neural network may include blood vessels such as retina blood vessels.

Abstract: Described herein are systems, methods, and instrumentalities associated with automatically detecting and enhancing multiple objects in medical scan images. The detection and/or enhancement may be accomplished utilizing artificial neural networks such as one or more classification neural networks and/or one or more graph neural networks. The neural networks may be used to detect areas in the medical scan images that may correspond to the objects of interest and cluster the areas belonging to a same object into a respective cluster. These tasks may be accomplished, for example, by representing the areas corresponding to the objects of interest and their interrelationships with a graph and processing the graph through the one or more graph neural networks so that the areas belonging to each object may be properly labeled and clustered. The clusters may then be used to enhance the objects of interests in one or more output scan images.

Abstract: Described herein are systems, methods, and instrumentalities associated with denoising medical images such as fluoroscopic images using deep learning techniques. A first artificial neural network (ANN) is trained to denoise an input medical image in accordance with a provided target noise level. The training of the first ANN is conducted by pairing a noisy input image with target denoised images that include different levels of noise. These target denoised images are generated using a second ANN as intermediate outputs of the second ANN during different training iterations. As such, the first ANN may learn to perform the denoising task in an unsupervised manner without requiring noise-free training images as the ground truth.

Abstract: Described herein are systems, methods, and instrumentalities associated with landmark detection. The detection may be accomplished by determining a graph representation of a plurality of hypothetical landmarks detected in one or more medical images. The graph representation may include nodes that represent the hypothetical landmarks and edges that represent the relationships between paired hypothetical landmarks. The graph representation may be processed using a graph neural network such a message passing graph neural network, by which the landmark detection problem may be converted and solved as a graph node labeling problem.

Abstract: An adhesive tape includes a composite membrane and an adhesive layer, where the composite membrane includes a substrate and a hardening layer provided on a surface of the substrate, and a hardness of the hardening layer is 3H to 9H. The adhesive tape provided in this application has both small thickness and high hardness, which increases volumetric energy density of the electrochemical apparatus and can further effectively avoid tape wrinkling of the electrochemical apparatus.

Abstract: Described herein are systems, methods and instrumentalities associated with automatic assessment of aneurysms. An automatic aneurysm assessment system or apparatus may be configured to obtain, e.g., using a pre-trained artificial neural network, strain values associated one or more locations of a human heart and one or more cardiac phases of the human heart and derive a representation (e.g., a 2D matrix) of the strain values across time and/or space. The system or apparatus may determine, based on the derived representation of the strain values, respective strain patterns associated with the one or more locations of the human heart and further determine whether the one or more locations are aneurysm locations by comparing the automatically determined strain patterns with predetermined normal strain patterns of the heart and determining the presence or risk of aneurysms based on the comparison.

Abstract: Described herein are systems, methods, and instrumentalities associated with generating multi-contrast MRI images associated with an MRI study. The systems, methods, and instrumentalities utilize an artificial neural network (ANN) trained to jointly determine MRI data sampling patterns for the multiple contrasts based on predetermined quality criteria associated with the MRI study and reconstruct MRI images with the multiple contrasts based on under-sampled MRI data acquired using the sampling patterns. The training of the ANN may be conducted with an objective to improve the quality of the whole MRI study rather than individual contrasts. As such, the ANN may learn to allocate resources among the multiple contrasts in a manner that optimizes the performance of the whole MRI study.

Abstract: This application discloses a decentralization processing method, which are applied to a microservice system. A first communication proxy in the microservice system receives a first microservice request sent by a first microservice application, the first communication proxy determines, based on a target control plane rule, a microservice access address corresponding to the first microservice request, and sends the first microservice request to the second communication proxy having the microservice access address, where the second communication proxy is used to forward the first microservice request to the second microservice application. The first communication proxy can send the microservice request to the corresponding second communication proxy, and a control plane does not need to route the microservice request, so that traffic for routing the microservice request by the control plane is effectively reduced, and impact of a burst of microservice requests on the control plane is effectively avoided.

Abstract: This application provides a video labeling method performed by a server, and the method includes: receiving a video extraction instruction transmitted by a terminal, and obtaining a to-be-extracted video according to the video extraction instruction; extracting event information from video frames of the to-be-extracted video; forming at least one event information flow by using the event information; capturing, based on the at least one event information flow, at least one first clip that meets a plot trigger condition from the to-be-extracted video and obtaining a plot labeling tag of the at least one first clip; and transmitting the at least one first clip and the corresponding plot labeling tag to the terminal, wherein the terminal displays the at least one first clip and the corresponding plot labeling tag in a preset display region of a display interface in which the to-be-extracted video is displayed.

Abstract: A model training method applied to the field of artificial intelligence is disclosed. The method includes: sending a first submodel to a first device, where the first submodel is obtained by compressing a to-be-trained model; receiving a first gradient sent by the first device, where the first gradient is obtained when the first device trains the first submodel; and performing model training on the to-be-trained model based on at least the first gradient, to obtain an updated to-be-trained model. In the method, a server compresses the to-be-trained model and delivers the to-be-trained model to a terminal device, so that the terminal device does not need to train a large model with a same scale as that of the server.

Abstract: Described herein are systems, methods, and instrumentalities associated with segmenting and/or determining the shape of an anatomical structure. An artificial neural network (ANN) is used to perform these tasks based on a statistical shape model of the anatomical structure. The ANN is trained by evaluating and backpropagating multiple losses associated with shape estimation and segmentation mask generation. The model obtained using these techniques may be used for different clinical purposes including, for example, motion estimation and motion tracking.

Abstract: A microfluidic apparatus, and its drive circuit and drive method are provided in the present disclosure. The drive circuit includes at least one switch unit, where the switch unit includes a first signal input terminal, a signal output terminal, first and second control signal terminals, a signal terminal, a first module, a second module, and a third module. The first module is electrically connected to the first signal input terminal, the signal output terminal and the second module; and the second module is electrically connected to the first control signal terminal. The first control signal terminal is configured to control the first module and the second module to be in conduction or disconnection, and the second control signal terminal is configured to control the third module to in conduction or disconnection, thereby controlling the signal output terminal to output a first or second signal.

Abstract: The present application relates to a posture display method, a device and a system for a guiding channel, and readable storage medium. The posture display method of a guiding channel includes determining an original position relation between a three-dimensional affected limb image and a virtual guiding channel displayed in a monitor according to an original position relation between the guiding channel and an affected limb and obtaining posture change data of the guiding channel. The method further includes adjusting the posture of the virtual guiding channel based on the posture change data, so as to the relative position relation between the virtual guiding channel and the three-dimensional affected limb image matches with the relative position relation between the guiding channel and the affected limb.

Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with cardiac assessment. An apparatus as described herein may obtain electrocardiographic imaging (ECGI) information associated with a human heart and magnetic resonance imaging (MRI) information associated with the human heart, and integrate the ECGI and MRI information using a machine-learned model. Using the integrated ECGI and MRI information, the apparatus may predict target ablation sites, estimate electrophysiology (EP) measurements, and/or simulate the electrical system of the human heart.

Abstract: In Multiplex MRI image reconstruction, a hardware processor acquires sub-sampled Multiplex MRI data and reconstructs parametric images from the sub-sampled Multiplex MRI data. A machine learning model or deep learning model uses the subsampled Multiplex MRI data as the input and parametric maps calculated from the fully sampled data, or reconstructed fully sample data, as the ground truth. The model learns to reconstruct the parametric maps directly from the subsampled Multiplex MRI data.

Abstract: A nuclear magnetic resonance (NMR) system-based substance measurement method, including: acquiring several echo signals of an NMR pulse sequence varying in echo spacing from a substance to be measured followed by processing to obtain several signals varying in transverse relaxation and diffusion attenuation; and fitting, in combination with the prior knowledge, the signals to obtain the diffusion coefficient, transverse relaxation time or/and content weight of individual components of the substance to be measured. This application further provides a substance measurement system including a console, a magnet module, and an NMR system.

Abstract: A neural network system implements a model for generating an output image based on a received input image. The model is learned through a training process during which parameters associated with the model are adjusted so as to maximize a difference between a first image predicted using first parameter values of the model and a second image predicted using second parameter values of the model, and to minimize a difference between the second image and a ground truth image. During a first iteration of the training process the first image is predicted and during a second iteration the second image is predicted. The first parameter values are obtained during the first iteration by minimizing a difference between the first image and the ground truth image, and the second parameter values are obtained during the second iteration by maximizing the difference between the first image and the second image.