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公开(公告)号：US20210374475A1
公开(公告)日：2021-12-02
申请号：US17402500
申请日：2021-08-13
Inventor： Xinrui Zhuang , Yuexiang Li , Yefeng Zheng
Abstract: An image recognition method includes: obtaining a target three-dimensional (3D) image; inputting the target 3D image to a first recognition model; and obtaining the image type of the target 3D image outputted by the first recognition model. The first recognition model is configured to perform image recognition on the target 3D image to obtain an image type of the target 3D image. A convolutional block of the first recognition model is the same as a convolutional block of a second recognition model, and configured to perform image recognition on the target 3D image. The second recognition model is obtained by training an original recognition model using a target training sample, the target training sample including cubes obtained by rotating and sorting N target cubes obtained from a 3D sample image, N being a natural number greater than 1.
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公开(公告)号：US12249076B2
公开(公告)日：2025-03-11
申请号：US17703829
申请日：2022-03-24
Inventor： Luyan Liu , Kai Ma , Yefeng Zheng
Abstract: A method for three-dimensional edge detection includes obtaining, for each of plural two-dimensional slices of a three-dimensional image, a two-dimensional object detection result and a two-dimensional edge detection result, stacking the two-dimensional object detection results into a three-dimensional object detection result, and stacking the two-dimensional edge detection results into a three-dimensional edge detection result. The method also includes performing encoding according to a feature map of the three-dimensional image, the three-dimensional object detection result, and the three-dimensional edge detection result, to obtain an encoding result, and performing decoding according to the encoding result, the three-dimensional object detection result, and the three-dimensional edge detection result, to obtain an optimized three-dimensional edge detection result of the three-dimensional image.
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公开(公告)号：US11854205B2
公开(公告)日：2023-12-26
申请号：US17229707
申请日：2021-04-13
Inventor： Shilei Cao , Kai Ma , Yefeng Zheng
CPC classification number: G06T7/11 , G06N3/045 , G06N3/08 , G16H30/40 , G06T2207/20081 , G06T2207/20084 , G06T2207/30004
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.
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公开(公告)号：US20240312022A1
公开(公告)日：2024-09-19
申请号：US18673627
申请日：2024-05-24
Inventor： Yifan HU , Yefeng Zheng
CPC classification number: G06T7/11 , G06N5/022 , G06T7/136 , G06T2207/20081
Abstract: The present disclosure provides methods, devices, apparatus, and storage medium for determining a target image region of a target object in a target image. The method includes: obtaining a target image comprising a target object; obtaining an original mask and an image segmentation model, the image segmentation model comprising a first unit model and a second unit model; downsampling the original mask based on a pooling layer in the first unit model to obtain a downsampled mask; extracting region convolution feature information of the target image based on a convolution pooling layer in the second unit model and the downsampled mask; updating the original mask according to the region convolution feature information; and in response to the updated original mask satisfying an error convergence condition, determining a target image region of the target object in the target image according to the updated original mask.
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5.发明授权公开(公告)号：US12056211B2
公开(公告)日：2024-08-06
申请号：US17501899
申请日：2021-10-14
Inventor： Yifan Hu , Yuexiang Li , Yefeng Zheng
IPC: G06V20/70 , A61B6/00 , G06F18/21 , G06F18/214 , G06N3/0455 , G06T7/10 , G06T7/143 , G06T9/00 , G06V20/69
CPC classification number: G06F18/2155 , A61B6/5294 , G06F18/2178 , G06N3/0455 , G06T7/10 , G06T7/143 , G06T9/002 , G06V20/695 , G06V20/70 , G06T2207/20112 , G06T2207/30004 , G06T2219/004
Abstract: A method for determining a target image to be labeled includes: obtaining an original image and an autoencoder (AE) set, the original image being an image having not been labeled, the AE set including N AEs; obtaining an encoded image set corresponding to the original image by using the AE set, the encoded image set including N encoded images, the encoded images being corresponding to the AEs; obtaining the encoded image set and a segmentation result set corresponding to the original image by using an image segmentation network, the image segmentation network including M image segmentation sub-networks, and the segmentation result set including [(N+1)*M] segmentation results; determining labeling uncertainty corresponding to the original image according to the segmentation result set; and determining whether the original image is a target image according to the labeling uncertainty.
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Patent Agency Ranking
6.发明授权公开(公告)号：US12016717B2
公开(公告)日：2024-06-25
申请号：US17246339
申请日：2021-04-30
Inventor： Heng Guo , Xingde Ying , Kai Ma , Yefeng Zheng
CPC classification number: A61B6/5223 , A61B6/466 , A61B6/5229 , G06T9/002
Abstract: A CT image generation method and apparatus, a computer device, and a computer-readable storage medium are presented. The method includes obtaining a first X-ray image and a second X-ray image, the first X-ray image and the second X-ray image being X-ray images acquired for a target object from two orthogonal viewing angles; calling a generator to perform three-dimensional reconstruction on the first X-ray image and the second X-ray image, to obtain a three-dimensional model of the target object; and obtaining a CT image of the target object according to the three-dimensional model of the target object.
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7.发明授权公开(公告)号：US11941807B2
公开(公告)日：2024-03-26
申请号：US17503160
申请日：2021-10-15
Inventor： Fubo Zhang , Dong Wei , Kai Ma , Yefeng Zheng
IPC: G06T7/00 , G06F18/214 , G06N3/04
CPC classification number: G06T7/0012 , G06F18/214 , G06N3/04 , G06T2207/10081
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.
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8.发明授权公开(公告)号：US11922638B2
公开(公告)日：2024-03-05
申请号：US17514467
申请日：2021-10-29
Inventor： Shilei Cao , Yifan Hu , Kai Ma , Yefeng Zheng
CPC classification number: G06T7/215 , G06N3/088 , G06T7/0012 , G06T2207/10081 , G06T2207/10088
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.
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公开(公告)号：US11908580B2
公开(公告)日：2024-02-20
申请号：US17397857
申请日：2021-08-09
Inventor： Yifan Hu , Yefeng Zheng
IPC: G16H50/20 , G16H30/40 , G06V10/25 , G06V10/26 , G06F18/21 , G06F18/22 , G06F18/214 , G06F18/25 , G06F18/2413 , G06V10/764 , G06V10/774 , G06V10/776 , G06V10/80 , G06V10/44 , G06N3/08
CPC classification number: G16H50/20 , G06F18/214 , G06F18/217 , G06F18/22 , G06F18/2413 , G06F18/253 , G06V10/25 , G06V10/26 , G06V10/454 , G06V10/764 , G06V10/774 , G06V10/776 , G06V10/806 , G16H30/40 , G06N3/08 , G06V2201/03
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.
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公开(公告)号：US12213828B2
公开(公告)日：2025-02-04
申请号：US17721806
申请日：2022-04-15
Inventor： Shilei Cao , Hualuo Liu , Yefeng Zheng
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.
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