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Patent Agency Ranking
公开(公告)号：US20220028087A1
公开(公告)日：2022-01-27
申请号：US17499993
申请日：2021-10-13
Inventor： Yifan HU , Yefeng ZHENG
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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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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3.发明申请公开(公告)号：US20220036135A1
公开(公告)日：2022-02-03
申请号：US17501899
申请日：2021-10-14
Inventor： Yifan HU , Yuexiang LI , Yefeng ZHENG
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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公开(公告)号：US20210365741A1
公开(公告)日：2021-11-25
申请号：US17397857
申请日：2021-08-09
Inventor： Yifan HU , Yefeng ZHENG
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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5.发明申请公开(公告)号：US20210241027A1
公开(公告)日：2021-08-05
申请号：US17204894
申请日：2021-03-17
Inventor： Yifan HU , Yefeng ZHENG
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.
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