Abstract: A method and system can be used for disease quantification modeling of an anatomical tree structure. The method may include obtaining a centerline of an anatomical tree structure and generating a graph neural network including a plurality of nodes based on a graph. Each node corresponds to a centerline point and edges are defined by the centerline, with an input of each node being a disease related feature or an image patch for the corresponding centerline point and an output of each node being a disease quantification parameter. The method also includes obtaining labeled data of one or more nodes, the number of which is less than a total number of the nodes in the graph neural network. Further, the method includes training the graph neural network by transferring information between the one or more nodes and other nodes based on the labeled data of the one or more nodes.

Abstract: The disclosure relates to systems and methods for evaluating a blood vessel. The method includes receiving image data of the blood vessel acquired by an image acquisition device, and predicting, by a processor, blood vessel condition parameters of the blood vessel by applying a deep learning model to the acquired image data of the blood vessel. The deep learning model maps a sequence of image patches on the blood vessel to blood vessel condition parameters on the blood vessel, where in the mapping the entire sequence of image patches contribute to the blood vessel condition parameters. The method further includes providing the blood vessel condition parameters of the blood vessel for evaluating the blood vessel.

Abstract: The present disclosure is directed to a computer-implemented method and system for anatomical tree structure analysis. The method includes receiving model inputs for a set of positions in an anatomical tree structure. The method further includes applying, by a processor, a set of encoders to the model inputs. Each encoder is configured to extract features from the model input at a corresponding position. The method also includes applying, by the processor, a tree structured network to the extracted features. The tree structured network has a plurality of nodes each connected to one or more of the encoders, and information propagates among the nodes of the tree structured network according to spatial constraints of the anatomical tree structure. The method additionally includes providing an output of the tree structured network as an analysis result of the anatomical tree structure analysis.

Abstract: The present disclosure relates to a method and a system for generating anatomical labels of an anatomical structure. The method includes receiving an anatomical structure with an extracted centerline, or a medical image containing the anatomical structure with the extracted centerline; and predicting the anatomical labels of the anatomical structure based on the centerline of the anatomical structure, by utilizing a trained deep learning network. The deep learning network includes a branched network, a Graph Neural Network, a Recurrent Neural Network and a Probability Graph Model, which are connected sequentially in series. The branched network includes at least two branch networks in parallel. The method in the disclosure can automatically generate the anatomical labels of the whole anatomical structure in medical image end to end and provide high prediction accuracy and reliability.

Abstract: A method and system can be used for disease quantification modeling of an anatomical tree structure. The method may include obtaining a centerline of an anatomical tree structure and generating a graph neural network including a plurality of nodes based on a graph. Each node corresponds to a centerline point and edges are defined by the centerline, with an input of each node being a disease related feature or an image patch for the corresponding centerline point and an output of each node being a disease quantification parameter. The method also includes obtaining labeled data of one or more nodes, the number of which is less than a total number of the nodes in the graph neural network. Further, the method includes training the graph neural network by transferring information between the one or more nodes and other nodes based on the labeled data of the one or more nodes.

Abstract: A semiconductor device and a method for manufacturing the semiconductor device. Multiple stacks and an isolation structure among the multiple stacks are formed on a substrate. Each stack includes a first doping layer, a channel layer and a second doping layer. For each stack, the channel layer is laterally etched from at least one sidewall of said stack to form a cavity located between the first doping layer and the second doping layer, and a gate dielectric layer and a gate layer are formed in the cavity. A first sidewall of each stack is contact with the isolation structure, and the at least one sidewall does not include the first side wall. Costly high-precision etching is not necessary, and therefore a device with a small size and a high performance can be achieved with a simple process and a low cost. Diversified device structures can be provided on requirement.

Abstract: Embodiments of the disclosure provide systems and methods for analyzing medical images using a learning model. The system receives a medical image acquired by an image acquisition device. The system may additionally include at least one processor configured to apply the learning model to perform an image analysis task on the medical image. The learning model is trained jointly with an error estimator using training images comprising a first set of labeled images and a second set of unlabeled images. The error estimator is configured to estimate an error of the learning model associated with performing the image analysis task.

Abstract: Embodiments of the disclosure provide systems and methods for medical image analysis. A method may include receiving a medical image acquired of a subject by an image acquisition device. The method may also include applying a calcium detection model to detect at least one calcium region relevant in determining a calcium score from the medical image. The method may further include applying a score regression learning model to the at least one calcium region to determine a calcium score for the medical image. The method may additionally include providing the determined calcium score of the medical image for a diagnosis of the subject.

Abstract: Embodiments of the disclosure provide systems and methods for biomedical image analysis. A method may include receiving a plurality of unannotated biomedical images, including a first image and a second image. The method may also include determining that the first image is in a first view and the second image is in a second view. The method may further include assigning the first image to a first processing path for the first orientation. The method may additionally include assigning the second image to a second processing path for the second view. The method may also include processing the first image in the first processing path in parallel with processing the second image in the second processing path. The first path may share processing parameters with the second path. The method may further include providing a diagnostic output based on the processing of the first image and the second image.

Abstract: Embodiments of the disclosure provide systems and methods for analyzing a medical image containing a vessel structure using a sequential model. An exemplary system includes a communication interface configured to receive the medical image and the sequential model. The sequential model includes a vessel extraction sub-model and a lesion analysis sub-model. The vessel extraction sub-model and the lesion analysis sub-model are independently or jointly trained. The exemplary system also includes at least one processor configured to apply the vessel extraction sub-model on the received medical image to extract location information of the vessel structure. The at least one processor also applies the lesion analysis sub-model on the received medical image and the location information extracted by the vessel extraction sub-model to obtain a lesion analysis result of the vessel structure. The at least one processor further outputs the lesion analysis result of the vessel structure.

Abstract: Embodiments of the disclosure provide methods and systems for joint abnormality detection and physiological condition estimation from a medical image. The exemplary method may include receiving, by at least one processor, the medical image acquired by an image acquisition device. The medical image includes an anatomical structure. The method may further include applying, by the at least one processor, a joint learning model to determine an abnormality condition and a physiological parameter of the anatomical structure jointly based on the medical image. The joint learning model satisfies a predetermined constraint relationship between the abnormality condition and the physiological parameter.

Abstract: The present disclosure relates to training methods for a machine learning model for physiological analysis. The training method may include receiving training data including a first dataset of labeled data of a physiological-related parameter and a second dataset of weakly-labeled data of the physiological-related parameter. The training method further includes training, by at least one processor, an initial machine learning model using the first dataset, and applying, by the at least one processor, the initial machine learning model to the second dataset to generate a third dataset of pseudo-labeled data of the physiological-related parameter. The training method also includes training, by the at least one processor, the machine learning model based on the first dataset and the third dataset, and providing the trained machine learning model for predicting the physiological-related parameter.

Abstract: A decoding method and device for Turbo product codes, a decoding device, a decoder and a computer storage medium are provided. The method includes: a received codeword of a Turbo product code is acquired, and iterative decoding is performed on the received codeword for a set first iterative decoding times (S101); a decoding result of iterative decoding performed for the first iteration times is judged according to a first decoding rule to obtain a decoding identifier representing the decoding result (S102); and error correction processing is performed on the Turbo product code on which iterative decoding is performed for the first iteration times according to the decoding identifier (S103).

Abstract: An application and a method of compounds inhibiting synthesis of very long chain fatty acids (VLCFAs) in preventing and controlling microbial pathogens are provided, which relate to the technical field of plant pathology and plant disease prevention and control. In particular, an application method of a compound for inhibiting the synthesis of VLCFAs in preventing and treating microbial pathogens is provided. Research results associated with the methods show that taking the synthesis of VLCFAs as the target, microbial pathogens can be inhibited by using compounds that inhibit the synthesis of VLCFAs. Therefore, the compounds inhibiting the synthesis of VLCFAs can be used in preventing and treating microbial pathogens diseases, which provides a new idea or strategy for the prevention and treatment of microbial pathogens diseases, and provides more choices for the types of drugs for the prevention and treatment of pathogenic diseases.

Abstract: A decoding method and device for Turbo product codes, a decoding device, a decoder and a computer storage medium are provided. The method includes: a received codeword of a Turbo product code is acquired, and iterative decoding is performed on the received codeword for a set first iterative decoding times (S101); a decoding result of iterative decoding performed for the first iteration times is judged according to a first decoding rule to obtain a decoding identifier representing the decoding result (S102); and error correction processing is performed on the Turbo product code on which iterative decoding is performed for the first iteration times according to the decoding identifier (S103).

Abstract: Provided are a staircase code decoding method and a staircase code decoding apparatus. The method includes: step 1, obtaining the length L of a sliding window, and continuously obtaining, starting from a P-th subcode block, L subcode blocks as first to-be-decoded subcode blocks in the sliding window; step 2, dividing the first to-be-decoded subcode blocks into a plurality of first to-be-decoded groups, respectively decoding the plurality of to-be-decoded groups to be decoded, and updating the plurality of first to-be-decoded groups according to a decoding result to obtain first updated subcode blocks; step 3, sliding the sliding window forwards by a length of N subcode blocks; step 4, dividing second to-be-decoded subcode blocks into a plurality of second to-be-decoded groups, decoding the plurality of second to-be-decoded groups, obtaining second updated subcode blocks, and outputting first M subcode blocks among the second updated subcode blocks; and step 5, sliding the sliding window backwards by S.

Abstract: A method for accessing a target application, where the method is applied to a terminal device on which a target application is installed, the target application is set with an application password to access the target application, the terminal device is set with first fingerprint information to unlock the terminal device, the terminal device is further set with an operation sequence corresponding to the first fingerprint information, and the operation sequence includes unlocking the terminal device and accessing the target application.

Abstract: Provided in the present disclosure are a method and device for decoding a Turbo product code, and a computer-readable storage medium. The method includes: acquiring a received codeword and a code pattern of the Turbo product code; determining a reduced error mode set according to the code pattern, a number of unreliable bits, and a reduced number of error bits, where the reduced number of error bits is less than the number of unreliable bits; calculating an optimal codeword according to the reduced error mode set and the received codeword; calculating extrinsic information by using a decoding algorithm according to the optimal codeword; and performing iterative computation according to the extrinsic information and a preset number of iterations to obtain a decoding result of the received codeword.

Abstract: A system offering simplified bi-directional video communication between a user and a device of a pre-configured one or more persons of interest includes a touch display with a pictorial representation of each of the one or more persons of interest and a pictorial representation of one or more health indicators. The touch display is configured to establish the bi-directional video communication with a selected one of said persons of interest in response to a single touch of the pictorial representation of the selected one of the persons of interest. In one implementation, the system includes one or more biometric telemetry devices for acquiring and transmitting biometric data associated with a specific health indicator to the touch display, which is then transmitted to a database, processed and accessed by one or more authorized persons. In another implementation, the system includes a workflow engine for healthcare management of the patient.

Abstract: Provided are a staircase code decoding method and a staircase code decoding apparatus. The method includes: step 1, obtaining the length L of a sliding window, and continuously obtaining, starting from a P-th subcode block. L subcode blocks as first to-be-decoded subcode blocks in the sliding window; step 2, dividing the first to-be-decoded subcode blocks into a plurality of first to-be-decoded groups, respectively decoding the plurality of to-be-decoded groups to be decoded, and updating the plurality of first to-be-decoded groups according to a decoding result to obtain first updated subcode blocks; step 3, sliding the sliding window forwards by a length of N subcode blocks; step 4, dividing second to-be-decoded subcode blocks into a plurality of second to-be-decoded groups, decoding the plurality of second to-be-decoded groups, obtaining second updated subcode blocks, and outputting first M subcode blocks among the second updated subcode blocks; and step 5, sliding the sliding window backwards by S.