Abstract: The present application discloses an image element matching method and apparatus, a model training method and apparatus, and a data processing method. The issue of finding image information matching a given image element is converted into the issue of predicting, from a matching knowledge graph, whether or not an edge is present between a node corresponding to the given image element and another node in the matching knowledge graph. Therefore, matching between image elements is flexibly implemented, matching performance is improved, and labor costs are reduced.

Abstract: Embodiments of the disclosure provide methods, apparatuses, and computer-readable media for generating image reports. In one embodiment, the method includes: obtaining an image to be analyzed; determining at least one reference image corresponding to the image to be analyzed, the at least one reference image corresponding to a respective reference image report; and generating, based on the reference image report, an image report corresponding to the image to be analyzed. In the method for generating an image report provided by the embodiments, by obtaining an image to be analyzed is obtained, at least one reference image corresponding to the image to be analyzed is determined; and an image report corresponding to the image to be analyzed is generated based on a respective reference image report corresponding to the at least one reference image.

Abstract: The specification discloses image and data processing methods and apparatuses. The method includes: obtaining a source pose and texture information according to a source image; obtaining a first synthetic image according to the source image, a target pose, and the source pose; obtaining a residual map according to the texture information and the first synthetic image; and obtaining a second synthetic image according to the first synthetic image and the residual map. The specification resolves the technical problem of lacking a sense of reality in a synthetic image due to loss of texture details in feature extraction during character action transfer in the existing technologies.

Abstract: The specification discloses image and data processing methods and apparatuses. The method includes: obtaining a source pose and texture information according to a source image; obtaining a first synthetic image according to the source image, a target pose, and the source pose; obtaining a residual map according to the texture information and the first synthetic image; and obtaining a second synthetic image according to the first synthetic image and the residual map. The specification resolves the technical problem of lacking a sense of reality in a synthetic image due to loss of texture details in feature extraction during character action transfer in the existing technologies.

Abstract: The present application discloses a method, device, and system for processing a medical image. The method includes obtaining a first image corresponding to a to-be-processed image, obtaining a second image corresponding to the to-be-processed image, wherein the first image and the second image are obtained in response to an image degradation processing being performed with respect to the to-be-processed image, and an image quality of the first image and an image quality of the second image are different. The method further includes obtaining image structural information corresponding to the to-be-processed image, and performing an image fusion processing with respect to the first image and the second image to image fusion based at least in part on the image structural information corresponding to the to-be-processed image, wherein a target image is obtained based at least in part on the image fusion processing, and the target image corresponds to the to-be-processed image.

Abstract: The specification discloses image and data processing methods and apparatuses. The method includes: obtaining a source pose and texture information according to a source image; obtaining a first synthetic image according to the source image, a target pose, and the source pose; obtaining a residual map according to the texture information and the first synthetic image; and obtaining a second synthetic image according to the first synthetic image and the residual map. The specification resolves the technical problem of lacking a sense of reality in a synthetic image due to loss of texture details in feature extraction during character action transfer in the existing technologies.

Abstract: The present application discloses an image element matching method and apparatus, a model training method and apparatus, and a data processing method. The issue of finding image information matching a given image element is converted into the issue of predicting, from a matching knowledge graph, whether or not an edge is present between a node corresponding to the given image element and another node in the matching knowledge graph. Therefore, matching between image elements is flexibly implemented, matching performance is improved, and labor costs are reduced.

Abstract: Embodiments of the disclosure provide methods, apparatuses, and computer-readable media for generating image reports. In one embodiment, the method includes: obtaining an image to be analyzed; determining at least one reference image corresponding to the image to be analyzed, the at least one reference image corresponding to a respective reference image report; and generating, based on the reference image report, an image report corresponding to the image to be analyzed. In the method for generating an image report provided by the embodiments, by obtaining an image to be analyzed is obtained, at least one reference image corresponding to the image to be analyzed is determined; and an image report corresponding to the image to be analyzed is generated based on a respective reference image report corresponding to the at least one reference image.

Abstract: Embodiments of the disclosure provide a method, apparatus, and device for data processing. The method comprises: obtaining data of at least two different modalities, the data comprising at least first data and second data, the first data and second stored in respective data sources; determining an overlapping keyword correlating the first data and the second data by analyzing textual descriptions associated with the first data and second data and identifying the overlapping keyword as appearing in a textual description of the first data and in a textual description of the second data; and storing the first data and the second data in an associative manner based on the overlapping keyword, the associative manner generated using the overlapping keyword.

Abstract: A method includes: determining a first neural network model; inputting an image to be processed containing a lung image to the first neural network model to obtain a lung lobe segmentation result of the image to be processed; wherein the first network layer in the first neural network model is configured to add an additional channel with coordinate information to input data input to the first network layer, and to determine output data of the first network layer based on the input data after the additional channel is added thereto.