Hon Hai Precision Industry Co., Ltd.

Tencent Technology (Shenzhen) Company Limited

Fu Tai Hua Industry (Shenzhen) Co., Ltd.

Huawei Technologies Co., Limited

Hong-Fu Jin Precision Industry (Shenzhen) Co., Ltd.

Byd Company Limited

Hongfujin Precision Electronics (Zhengzhou) Co., Ltd.

Foxconn Technology Co., Ltd.

Fu Zhun Precision Industry (Shen Zhen) Co., Ltd.

Zte Corporation

Huawei Device Co., Ltd.

Johnson Electric International Ag

Other

Xi'an Zhongxing New Software Co., Ltd.

Huawei Digital Power Technologies Co., Ltd.

Tao Li

Tsung-Jen Chuang

Shih-Fang Wong

Zheng Shi

Chien-Li Tsai

Chun-Chi Liang

Yang Xin

Liang Wang

Yalin Liu

Yan Zhang

Xin Hui Zhang

Lei Shi

Wei Wu

Yong Fan

Xiao Han

Guang-Yao Lee

Min Yan

Jun Xu

Hui Jin

Jin Xu

Bo Sun

Wei Li

Junfeng Zhang

Bin Wang

Jun-Wei Zhang

Wan-Lin Xia

Yubo Lian

Dianbo Fu

Wei Gang Bao

Hao Li

Jia Chen

Eyal Shlomot

Mingchao Li

Hui Zhang

Yun Chen

Jin Yang

Zhifeng Yuan

Guanglin Han

Yang Zhao

Qiang Yi

Yi Tong Wang

Jia Tao Li

Kaibo Tian

Fei Ye

Huafeng Lin

Zhenyu Xu

Hui Li

Lian Zhang

Wa Ye

Wei Jiang

Yiling Wu

Junfeng Zhao

Qi-Long Yu

Yi-Tao Kao

Wen-Kang Lo

Ren-Wen Huang

Guodong Xue

Xin Tan

Kuan Tian

Qiang You

Jianhua Yao

Qiong Wang

Lin-Kun Ding

Liguang Li

Xin Huang

Chuanfu Wang

Xiaohu You

Yiping Guo

Jun Yuan

Chia-Hung Chien

Ming Ju Chen

Liqiang Wang

Haijun Wu

Zhe Wang

Cheng Chen

Xu Zhang

Zheng Wang

Ran Chen

Yong Lin

Dacheng Zhuo

Hongyang Wang

Wen Shi

Xin Zhi Ma

Yin-Zhan Wang

Xiangyang Wu

Jinliang Dai

Min-Qi Xiao

Jiezuo Zhu

Kai Yan

Haijun Wang

Hua Dong

Zhaoyang Li

Jiewei Ding

Yongsheng Wang

Mengsha Zhou

Xin Zhou

Jiansheng Wei

Jianhui Yao

An image processing method includes: acquiring a standard image matched with an image processing model; determining light field variation parameters of a use environment of the image processing model based on the standard image; determining an image training sample set based on the light field variation parameters, the image training sample set including images of different light field backgrounds; training a correction network of the image processing model through the image training sample set to obtain a model updating parameter adapted to the correction network, and generating a trained image processing model based on the model updating parameter; acquiring a single image in the use environment, and performing prediction processing on the single image through the trained image processing model to obtain light field information corresponding to the single image; and correcting a light field of the single image based on the light field information.

Image processing method and apparatus based on image processing model, electronic device, storage medium, and computer program product

Provided is a method for searching for a whole slide image performed by a computer device, which relate to the field of artificial intelligence. The method includes: cropping a whole slide image into a plurality of tissue images; generating, through an image encoder, image feature vectors respectively corresponding to the plurality of tissue images; clustering the image feature vectors respectively corresponding to the plurality of tissue images, to determine at least one key image from the plurality of tissue images; querying, based on image feature vectors respectively corresponding to the at least one key image, a database to obtain at least one target image package corresponding to the at least one key image; and determining a whole slide image to which at least one candidate tissue image comprised in the at least one target image package respectively belongs as a final search result.

Image encoder training method and apparatus, device, and medium

XLR cable

The present disclosure provides a medical image segmentation method. The medical image segmentation method includes acquiring a to-be-processed medical image set, the to-be-processed medical image set including a plurality of to-be-processed medical images corresponding to different time points, processing the to-be-processed medical image set in a time dimension according to the to-be-processed medical images and the time points corresponding to the to-be-processed medical images to obtain a temporal dynamic image, and extracting a target region feature from the temporal dynamic image by using a medical image segmentation model, to acquire a target region.

Medical image segmentation method, image segmentation method, and related apparatus and system

Embodiments of this application disclose a method and apparatus for training an image region segmentation model, and an image region segmentation method and apparatus. The method includes acquiring a sample image set, and each image of the sample image set having first annotation information; generating graph structure data corresponding to a sample image in the sample image set, the graph structure data comprising multiple nodes, and each node comprising at least one pixel in the sample image; determining second annotation information of each node according to the graph structure data and the first annotation information corresponding to the sample image by using a graph convolutional network model, a granularity of the second annotation information being smaller than a granularity of the first annotation information, the graph convolutional network model being a part of an image segmentation model; and training the image segmentation model according to the second annotation information.

Training method and apparatus for image region segmentation model, and image region segmentation method and apparatus

The disclosure provides an image processing method and apparatus. The method may include acquiring a target image having at least two division regions. The division region includes an annotation point, and the annotation point is corresponding to a target segmentation object. The method may further include performing iterative expansion on the annotation points to obtain an iterative expansion region. The method may further include determining a first loss result between the iterative expansion region and the division region and a second loss result between the iterative expansion region and the annotation point. The method may further include, when the first loss result and the second loss result meeting a termination condition, stopping the iterative expansion to obtain a resulting iterative expansion region. The method may further include determining the resulting iterative expansion region as a region where the target segmentation object is located in the target image.

Image processing method and apparatus for segmenting objects

Embodiments of this application disclose methods, systems, and devices for image region localization and medical image processing. In one aspect, a method comprises acquiring three-dimensional images of a target body part of a patient. The three-dimensional images comprise a plurality of magnetic resonant imaging (MRI) modalities. The method comprises registering a first image set of a first modality with a second image set of a second modality. After the registering, image features of the three-dimensional images are extracted. The image features are fused to obtain fused features. The method also comprises determining voxel types corresponding to voxels in the three-dimensional images according to the fused features. The method also comprises selecting, from the three-dimensional images, target voxels having a preset voxel type, obtaining position information of the target voxels, and localizing a target region within the target body part based on the position information of the target voxels.

Image region localization method, image region localization apparatus, and medical image processing device

A method for generating a special effect for social networking interaction in a virtual environment of a game is performed by an electronic device. The method includes: displaying an object presentation interface of a target battle of the game when loading a virtual scene corresponding to the target battle, the object presentation interface being used for displaying a plurality of virtual objects participating in the target battle; receiving a special effect generating instruction for a first virtual object of the plurality of virtual objects, the special effect generating instruction being used for instructing to generate a special effect based on the first virtual object, and the first virtual object corresponding to a user of the electronic device triggering the special effect; and generating the special effect identifying the first virtual object in the object presentation interface.

Method and apparatus for generating special effect in virtual environment, device, and storage medium

This application provides a pathological section image processing method performed by a computer device. The method includes: obtaining stained images of a pathological section after cell membrane staining; determining cell nucleus positions of cancer cells in a stained image under an ifield of view in the n fields of view; generating a cell membrane description result of the stained image under the ifield of view, the cell membrane description result being used for indicating completeness and staining intensity of the cell membrane staining; determining quantities of cells of types in the stained image under the ifield of view according to the cell nucleus positions and the cell membrane description result; and determining an analysis result of the pathological section according to quantities of the cells of types in the stained images under the n fields of view.

Pathological section image processing method and apparatus, system, and storage medium

An image processing method includes: obtaining DCE magnetic resonance images corresponding to a plurality of time points for a same detection target; determining average pixel grayscale values of images of a same lesion region in the DCE magnetic resonance images of the plurality of time points respectively; determining a time to peak according to the average pixel grayscale values corresponding to the plurality of time points; and generating a first-stage time-intensity image before the time to peak and a second-stage time-intensity image after the time to peak respectively according to the DCE magnetic resonance images and the time to peak. The first-stage time-intensity image and the second-stage time-intensity image are 3D images. A pixel grayscale value of each pixel in the first-stage time-intensity image and the second-stage time-intensity image represents a change rate of blood supply intensity and reflects a severity level of a lesion corresponding to the lesion region.

Image processing method, apparatus, and system, electronic device, and storage medium

Scooter

Disclosed are a distributed database preprocessing method, a database proxy layer, a system and a non-transitory computer-readable storage medium. The distributed database preprocessing method, applied to a database proxy layer, may include: receiving a request corresponding to a prepared statement; and performing processing according to the request corresponding to the prepared statement and a session identifier cached in a prepared statement cache library, a session corresponding to the session identifier cached in the prepared statement cache library is a prepared statement session where a connection is successfully established between a client and a database.

Distributed database preprocessing method, agent layer, system and storage medium

Game controller

Growing community of inventors

Shenzhen, China

Tao Li