Abstract: Embodiments of this application provide an electronic device display method and an electronic device. The electronic device includes a foldable body, an outer screen, and an inner screen, and the method includes: when the foldable body is in a folded state, detecting whether a protective cover approaches the outer screen and reaches a preset position with respect to the outer screen; if the protective cover reaches the preset position with respect to the outer screen, when the preset condition is met, determining whether the foldable body is switched to a non-folded state; and if the foldable body is switched to the non-folded state, controlling the electronic device to be in a screen non-locked state. That the preset condition is met includes at least being within a preset time after the protective cover reaches the preset position.

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: 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: 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 relates to an image data inspection method and apparatus in the field of artificial intelligence (AI) technologies. The method includes obtaining an image to be inspected, the image to be inspected comprising a sequence of slice images; determining a corresponding group of slice images for each target image in the sequence of slice images; extracting a corresponding slice feature map for each slice image in the group of slice images; aligning the slice feature maps extracted corresponding to the group of slice images; aggregating context information of each slice image in the group of slice images by using an aligned feature map; and performing target region inspection on an aggregated feature map, to obtain an inspection result corresponding to the target image, and combining the inspection result corresponding to each target image, to generate an inspection result corresponding to the image to be inspected.

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: 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: 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.