Abstract: An image segmentation method is provided for a computing device. The method includes obtaining a general tumor image, performing tumor localization on the tumor image to obtain a candidate image indicating a position of a tumor region in the general tumor image, inputting the candidate image to a cascaded segmentation network constructed based on a machine learning model, and performing image segmentation on the general tumor region in the candidate image using a first-level segmentation network and a second-level segmentation network in the cascaded segmentation network to obtain a segmented image.

Abstract: The present disclosure provides an artificial intelligence-based (AI-based) medical image processing method performed by a computing device, and a non-transitory computer-readable storage medium. The AI-based medical image processing method includes: processing a medical image to generate an encoded intermediate image; processing the encoded intermediate image, to segment a first feature and generate a segmented intermediate image; processing the encoded intermediate image and the segmented intermediate image based on an attention mechanism, to generate a detected intermediate input image; and performing second feature detection on the detected intermediate input image, to determine whether an image region of the detected intermediate input image in which the first feature is located comprises a second feature.

Abstract: A source-network-load-storage coordination dispatching method in a background of a coupling of renewable energy sources, including: taking an expectation of a minimum grid operating cost in a dispatching cycle as an objective function; generating an approximate value function of an output of a set for generating electricity from renewable energy sources and a user load, and constructing a source-network-load-storage coordination dispatching model with combination of the objective function; obtaining forecast data of the output of a set for generating electricity from renewable energy sources and the user load, and inputting the forecast data into the dispatching model for solving; performing iterative updating on the approximate value function, importing the approximate value function after the iterative updating into the dispatching model for iterative solving, and terminating an iterative process until a solving result satisfies a preset convergence condition; and using a solving result of a last iteration

Abstract: An image generation method includes obtaining a modality image corresponding to a first modality, and performing modality conversion on the modality image through a first candidate network to obtain a generated image corresponding to a second modality different from the first modality. The generated image is a three-dimensional image. The method further includes performing modality restoration on the generated image through a second candidate network to obtain a restored image corresponding to the first modality and obtaining a constraint loss value based on a modality conversion effect of the generated image and a modality restoration effect of the restored image. The constraint loss value indicates a mapping loss in mapping the modality image to a three-dimensional image space by the first candidate network. The method also includes training the first candidate network based on the constraint loss value to obtain an image conversion network.

Abstract: The disclosure relates to methods, devices, systems, and computer storage medium for performing medical image segmentation. The method includes: The method includes: obtaining, by a device, a first medical image and a second medical image with a labeled region; performing, by the device, feature extraction on the first medical image and the second medical image respectively, to obtain first feature information of the first medical image and second feature information of the second medical image; obtaining, by the device, optical flow motion information from the second medical image to the first medical image according to the first feature information and the second feature information; and segmenting, by the device, the first medical image according to the optical flow motion information and the labeled region, to obtain a segmentation result of the first medical image.

Abstract: A computer device obtains a plurality of medical images. The device generates a texture image based on image data of a region of interest in the medical images. The device extracts a local feature from the texture image using a first network model. The device extracts a global feature from the medical images using a second network model. The device fuses the extracted local feature and the extracted global feature to form a fused feature. The device performs image classification based on the fused feature.

Abstract: Embodiments of this disclosure include a method and an apparatus for segmenting a medical image. The method may include obtaining a slice pair comprising two slices and performing feature extraction on each slice in the slice pair, to obtain high-level feature information and low-level feature information of the each slice in the slice pair. The method may further include segmenting a target object in the each slice according to the low-level feature information and the high-level feature information of the slice, to obtain an initial segmentation result of the each slice and fusing the low-level feature information and the high-level feature information of the slices to obtain a fused feature information. The method may further include determining correlation information between the slices according to the fused feature information and generating a segmentation result of the slice pair based on the correlation information and the initial segmentation results of the slices.

Abstract: For three-dimensional segmentation from two-dimensional intracardiac echocardiography imaging, the three-dimension segmentation is output by a machine-learnt multi-task generator. Rather than the brute force approach of training the generator from 2D ICE images to output a 2D segmentation, the generator is trained from 3D information, such as a sparse ICE volume assembled from the 2D ICE images. Where sufficient ground truth data is not available, computed tomography or magnetic resonance data may be used as the ground truth for the sample sparse ICE volumes. The generator is trained to output both the 3D segmentation and a complete volume (i.e., more voxels represented than in the sparse ICE volume). The 3D segmentation may be further used to project to 2D as an input with an ICE image to another network trained to output a 2D segmentation for the ICE image. Display of the 3D segmentation and/or 2D segmentation may guide ablation of tissue in the patient.

Abstract: This application relates to a medical image segmentation method, a computer device, and a storage medium. The method includes: obtaining medical image data; obtaining a target object and weakly supervised annotation information of the target object in the medical image data; determining a pseudo segmentation mask for the target object in the medical image data according to the weakly supervised annotation information; and performing mapping on the medical image data by using a preset mapping model based on the pseudo segmentation mask, to obtain a target segmentation result for the target object. Because the medical image data is segmented based on the weakly supervised annotation information, there is no need to annotate information by using much labor during training of the preset mapping model, thereby saving labor costs. The preset mapping model is a model used for mapping the medical image data based on the pseudo segmentation mask.

Abstract: This application provides a gesture information processing method and apparatus, an electronic device, and a storage medium. The method includes: acquiring an electromyography signal sample generated by an electromyography signal collection target object in connection with performing multiple gestures; dividing the electromyography signal sample through a sliding window having a fixed window value and a fixed stride into different electromyography signals of the target object; and applying the different electromyography signals to a first neural network model to determine gesture information matching the multiple gestures performed by the target object.

Abstract: For three-dimensional segmentation from two-dimensional intracardiac echocardiography imaging, the three-dimension segmentation is output by a machine-learnt multi-task generator. Rather than the brute force approach of training the generator from 2D ICE images to output a 2D segmentation, the generator is trained from 3D information, such as a sparse ICE volume assembled from the 2D ICE images. Where sufficient ground truth data is not available, computed tomography or magnetic resonance data may be used as the ground truth for the sample sparse ICE volumes. The generator is trained to output both the 3D segmentation and a complete volume (i.e., more voxels represented than in the sparse ICE volume). The 3D segmentation may be further used to project to 2D as an input with an ICE image to another network trained to output a 2D segmentation for the ICE image. Display of the 3D segmentation and/or 2D segmentation may guide ablation of tissue in the patient.

Abstract: An image segmentation model training method includes acquiring a first image, a second image, and a labeled image of the first image; acquiring a first predicted image according to a first network model; acquiring a second predicted image according to a second network model; determining a reference image of the second image based on the second image and the labeled image of the first image; and updating a model parameter of the first network model based on the first predicted image, the labeled image, the second predicted image, and the reference image to obtain an image segmentation model.

Abstract: This application provides a gesture information processing method and apparatus, an electronic device, and a storage medium. The method includes: determining an electromyography signal collection target object in a gesture information usage environment; dividing the electromyography signal sample through a sliding window having a fixed window value and a fixed stride into different electromyography signals of the target object, and denoising the different electromyography signals of the target object; recognizing the denoised different electromyography signals, and determining probabilities of gesture information represented by the different electromyography signals; and weighting the probabilities of the gesture information represented by the different electromyography signals, so as to determine gesture information matching the target object.

Abstract: Embodiments of this application disclose a brain image segmentation method and apparatus, a network device, and a storage medium. The method includes obtaining, by a device, a to-be-segmented image group comprising a plurality of modal images of a brain. The device includes a memory storing instructions and a processor in communication with the memory. The method further includes performing, by the device, skull stripping according to the plurality of modal images to obtain a skull-stripped mask; separately performing, by the device, feature extraction on the plurality of modal images to obtain extracted features, and fusing the extracted features to obtain a fused feature; segmenting, by the device, encephalic tissues according to the fused feature to obtain an initial segmentation result; and fusing, by the device, the skull-stripped mask and the initial segmentation result to obtain a segmentation result corresponding to the to-be-segmented image group.

Abstract: For three-dimensional segmentation from two-dimensional intracardiac echocardiography imaging, the three-dimension segmentation is output by a machine-learnt multi-task generator. Rather than the brute force approach of training the generator from 2D ICE images to output a 2D segmentation, the generator is trained from 3D information, such as a sparse ICE volume assembled from the 2D ICE images. Where sufficient ground truth data is not available, computed tomography or magnetic resonance data may be used as the ground truth for the sample sparse ICE volumes. The generator is trained to output both the 3D segmentation and a complete volume (i.e., more voxels represented than in the sparse ICE volume). The 3D segmentation may be further used to project to 2D as an input with an ICE image to another network trained to output a 2D segmentation for the ICE image. Display of the 3D segmentation and/or 2D segmentation may guide ablation of tissue in the patient.

Abstract: A data classification and recognition method includes: obtaining a first data set and a second data set, the second data set including second data, samples in the second data being labeled; performing training using first data in an unsupervised training mode and using the second data in a supervised training mode to obtain a first classification model; obtaining a second classification model; performing distillation training on a model parameter of the second classification model to obtain a data classification model; and performing class prediction on target data by using the data classification model.

Abstract: A data processing method includes: acquiring an initial sample angiography image set; performing data expansion processing on a first sample angiography image based on physical characteristics of blood vessels at a target site to obtain a processed sample angiography image, performing label conversion processing on a first label based on the physical characteristics of the blood vessels at the target site to obtain a second label of the processed sample angiography image, and adding the processed sample angiography image and the second label to a target sample angiography image set; and training an angiography image recognition model using the initial sample angiography image set and the target sample angiography image set to obtain a trained angiography image recognition model. The performance of the trained angiography image recognition model is improved by increasing the number of samples.

Abstract: An image segmentation method includes: encoding an original image containing a target object based on a prior knowledge vector, to obtain a target feature map, the prior knowledge vector comprising a plurality of prior knowledge weights each representing accuracy of a corresponding rater labeling a region of an object in an image; decoding the target feature map, to obtain a first segmented image of the original image, the first segmented image indicating a target region in which the target object is located in the original image; performing image reconstruction on the first segmented image based on the prior knowledge vector, to obtain labeled segmented images, wherein one labeled segmented image corresponds to one prior knowledge weight and indicates a target region labeled by a corresponding rater; and processing the target feature map based on the labeled segmented images, to obtain a second segmented image of the original image.

Abstract: This present disclosure relates to the technical field of artificial intelligence, and provides a vessel image classification method and apparatus, a device, and a storage medium. The method includes: inputting a first vessel image sample into a first image processing model, and obtaining a predicted enhanced image and predicted vessel location information; and training the first image processing model based on a second vessel image sample, vessel location labeling information, the predicted enhanced image, and the predicted vessel location information. In the above solution, the impact of image quality on the vessel classification is considered during training of the vessel classification model, so that an end-to-end vessel classification model subsequently generated based on the trained first image processing model can realize a higher classification accuracy for a low quality vessel image, thereby improving the accuracy of classifying vessels in the vessel image by artificial intelligence.

Abstract: An image processing method can reduce costs related to manual labeling, improve training efficiency, and increase a quantity of training samples, thereby improving the accuracy of an image classification model. First images and second images are processed using an image classification model to obtain predicted classification results. The first images include a classification label and the second images include a pseudo classification label. A first loss value indicating accuracy is acquired based on the predicted classification results, the corresponding classification labels, and the corresponding pseudo classification labels. A second loss value indicating accuracy is acquired based on the predicted classification results and the corresponding pseudo classification labels. A model parameter of the image classification model is updated based on the first loss value and the second loss value. Classification processing and acquisition is performed until a target image classification model is obtained.