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公开(公告)号：US12046056B2
公开(公告)日：2024-07-23
申请号：US17379839
申请日：2021-07-19
Inventor： Hu Ye , Xiao Han , Kaiwen Xiao , Niyun Zhou , Mingyang Chen
CPC classification number: G06V20/698 , G06T3/147 , G06T7/0012 , G06V10/945 , G16H30/40
Abstract: A computer device obtains a to-be-annotated image having a first magnification. The device obtains an annotated image from an annotated image set, the annotated image distinct from the to-be-annotated image and having a second magnification that is distinct from with the first magnification. The annotated image set includes at least one annotated image. The device matches the to-be-annotated image with the annotated image to obtain an affine transformation matrix, and generates annotation information of the to-be-annotated image according to the affine transformation matrix and the annotated image. In this way, annotations corresponding to images at different magnifications may be migrated. For example, the annotations may be migrated from the low-magnification images to the high-magnification images, thereby reducing the manual annotation amount and avoiding repeated annotations, and further improving annotation efficiency and reducing labor costs.
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公开(公告)号：US11995827B2
公开(公告)日：2024-05-28
申请号：US17718224
申请日：2022-04-11
Inventor： Zhaoxuan Ma , De Cai , Hu Ye , Xiao Han , Yanqing Kong , Hongping Tang
IPC: G06T7/00 , G06T11/00 , G06V10/75 , G06V10/764 , G06V20/69
CPC classification number: G06T7/0012 , G06T11/00 , G06V10/75 , G06V10/764 , G06V20/695 , G06T2207/30024 , G06T2207/30096 , G06T2210/41
Abstract: An image display method includes: processing a first image to obtain a first feature image, the first image being an image of a local area of a smear captured by a microscope, and the local area including multiple objects to be tested; obtaining a second feature image corresponding to the first feature image, the second feature image and the first feature image having a same size; obtaining a third feature image according to an image obtained by overlaying the first feature image and the second feature image, a feature point in the third feature image indicating a possibility that one of the multiple objects is an abnormal object; obtaining a second image according to the third feature image; and displaying the second image superimposed on the first image.
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3.发明授权公开(公告)号：US11954852B2
公开(公告)日：2024-04-09
申请号：US17375177
申请日：2021-07-14
Inventor： Kaiwen Xiao , Xiao Han , Hu Ye , Niyun Zhou
IPC: G06T7/00 , G06T3/4007 , G06T7/90 , G16H30/20 , G16H30/40
CPC classification number: G06T7/0012 , G06T3/4007 , G06T7/90 , G16H30/20 , G16H30/40 , G06T2207/10056 , G06T2207/20076 , G06T2207/20081 , G06T2207/20084 , G06T2207/30004 , G06T2207/30168
Abstract: This application describes a medical image classification method, a model training method, and a server. The medical image classification method includes: obtaining, by a device, a medical image data set. The device includes a memory storing instructions and a processor in communication with the memory. The method includes performing, by the device, quality analysis on the medical image data set, to extract feature information of a medical image in the medical image data set; and classifying, by the device, the medical image data set based on the feature information and by using a pre-trained deep learning network for performing anomaly detection and classification, to obtain a classification result.
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4.发明授权公开(公告)号：US12223645B2
公开(公告)日：2025-02-11
申请号：US17701910
申请日：2022-03-23
Inventor： De Cai , Hu Ye , Zhaoxuan Ma , Xiao Han
Abstract: Methods, apparatus, device, and storage medium for identifying an abnormal cell in a to-be-detected sample are disclosed. The method includes obtaining, by a device, multi-layer images of a to-be-detected sample, the to-be-detected sample comprising a single cell and a cell cluster; obtaining, by the device, multi-layer image blocks of the single cell and multi-layer image blocks of the cell cluster according to the multi-layer images; obtaining, by the device, a first identification result by a first image identification network according to the multi-layer image blocks of the single cell; obtaining, by the device, a second identification result by a second image identification network according to the multi-layer image blocks of the cell cluster; and determining, by the device, whether an abnormal cell exists in the to-be-detected sample according to the first identification result and the second identification result.
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