Hon Hai Precision Industry Co., Ltd.

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

Tencent Technology (shenzhen) Company Limited

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

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

Xiao Han

Yubo Lian

Dianbo Fu

Wei Gang Bao

Hao Li

Jia Chen

Eyal Shlomot

Hui Zhang

Yun Chen

Jin Yang

Zhifeng Yuan

Yang Zhao

Yi Tong Wang

Qiang Yi

Jia Tao Li

Kaibo Tian

Huafeng Lin

Zhenyu Xu

Hui Li

Lian Zhang

Wa Ye

Fei Ye

Junfeng Zhao

Qi-Long Yu

Yi-Tao Kao

Wen-Kang Lo

Ren-Wen Huang

Guodong Xue

Xin Tan

Qiang You

Xiaohu You

Lin-Kun Ding

Yiping Guo

Kuan Tian

Chia-Hung Chien

Xin Huang

Chuanfu Wang

Jianhua Yao

Qiong Wang

Jun Yuan

Yue-Hai Zhang

Wei Jiang

Liguang Li

Haijun Wu

Ming Ju Chen

Liqiang Wang

Zheng Wang

Cheng Chen

Zhe Wang

Xu Zhang

Ran Chen

Ka Dan

Yong Lin

Dacheng Zhuo

Hongyang Wang

Wen Shi

Yin-Zhan Wang

Xin Zhi Ma

Jinliang Dai

Ji-Yun Qin

Min-Qi Xiao

Kai Yan

Hua Dong

Xin Zhou

Mengsha Zhou

Jiewei Ding

Jiansheng Wei

Yongsheng Wang

Liqun Deng

Chongyang Wang

Na Li

Zhaoyang Li

Zhigang Song

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

Tricycle

The present invention provides an application of nano copper in a cutting fluid. The nano copper is self-dispersible nano copper with an organic long-carbon chain, wherein the surface of copper metal is coated with a long carbon chain organic matter, and the long chain organic matter is dialkyl dithiophosphoric acid (HDDP) and a derivative thereof. In the present invention, the nano copper substitutes functional additives which comprises one or more of a preservative, an anti-rust agent, a sterilizing agent, a compression-resisting agent and a lubricant to solve the technical problems of the existing fluid in the prior art being unable to simultaneously have efficient anti-corrosion, anti-rust, compression-resistant, lubricating and sterilizing properties as well as the variety, the relatively high amount, the high cost and the limited selection of added additives.

Application of nano copper in cutting fluid

Growing community of inventors

Shenzhen, China

Tao Li