Tencent Technology (shenzhen) Company Limited

Honor Device Co., Ltd.

Guangdong Galanz Microwave Oven and Electrical Appliances Manufacturing Co., Ltd.

Tencent Technology Company Limited

Cheng Jiang

Liang Liu

Yu Wang

Kuan Tian

Rongbo Shen

Jianhua Yao

Jie Li

Liang Wang

Xuan Zhou

Dan Chen

Fen Xiao

Jian Zhu

Jing Gao

Kezhou Yan

Jiarui Sun

Ke Zhou

Yanchun Zhu

Ke Yan

Wei Zhai

Jia Chang

Zhigang Li

Yin Meng

Zhihua Chen

Zengchao Qu

Tianyu Pen

Chunhui Ye

Dongping Liu

Aspects of the disclosure are directed to the field of artificial intelligence technologies and provides a method and an apparatus for obtaining a feature of duct tissue based on computer vision, an intelligent microscope, a storage medium, and a computer device. The method can include the steps of obtaining an image including duct tissue, determining, in an image region corresponding to the duct tissue in the image, at least two feature obtaining regions adapted to duct morphology of the duct tissue, obtaining cell features of cells of the duct tissue in the feature obtaining regions respectively, and obtaining a feature of the duct tissue based on the cell features of the cells of the duct tissue in the feature obtaining regions respectively.

Method and apparatus for obtaining feature of duct tissue based on computer vision, and intelligent microscope

A radio frequency conduction test method and a test system are provided. The test method includes: moving a radio frequency test probe to a first pad of a board so as to allow a test signal on the first pad to be transmitted to the radio frequency test probe and then transmitted to a radio frequency test instrument for a radio frequency conduction test, where the test signal in the radio frequency test probe is transmitted to the radio frequency test instrument via an impedance conversion apparatus and a directional coupler, a straight-through output port of the directional coupler is connected to a first measurement port of the radio frequency test instrument, and a coupling output port of the directional coupler is connected to a second measurement port of the radio frequency test instrument.

Radio frequency conduction test method and related apparatus without a radio frequency switch test socket

An AI-based object classification method and apparatus, a computer-readable storage medium, and a computer device. The method includes: obtaining a target image to be processed, the target image including a target detection object; separating a target detection object image of the target detection object from the target image; inputting the target detection object image into a feature object prediction model to obtain a feature object segmentation image of a feature object in the target detection object image; obtaining quantitative feature information of the target detection object according to the target detection object image and the feature object segmentation image; and classifying the target detection object image according to the quantitative feature information to obtain category information of the target detection object in the target image.

Ai-based object classification method and apparatus, and medical imaging device and storage medium

The present disclosure relates to a method for training a neural network model performed at an electronic device. The method includes: performing initial training by using a first training sample set to obtain an initial neural network model; performing a prediction on a second training sample set by using the initial neural network model to obtain a prediction result of each of training samples in the second training sample set; determining a plurality of preferred samples from the second training sample set based on the prediction results; adding the plurality of preferred samples that are annotated to the first training sample set to obtain an expanded first training sample set; updating training of the initial neural network model by using the expanded first training sample set to obtain an updated neural network model until a training ending condition is satisfied.

Method, apparatus, and electronic device for training neural network model

A medical image region screening method and apparatus and a storage medium are provided. The method includes: obtaining a medical image of biological tissue, segmenting tissue regions of a plurality of tissue types from the medical image, selecting, from the tissue regions of the plurality of tissue types based on types of capturing positions of the medical image, a reserved region, obtaining a positional relationship between the reserved region and a predicted lesion region in the medical image; and screening for the predicted lesion region in the medical image based on the positional relationship, to obtain a target lesion region.

Medical image region screening method and apparatus and storage medium

A medical image processing method includes: obtaining a biological tissue image including a biological tissue, recognizing, in the biological tissue image, a first region of a lesion object in the biological tissue; recognizing a lesion attribute matching the lesion object; dividing an image region of the biological tissue in the biological tissue image into a plurality of quadrant regions; obtaining target quadrant position information of a quadrant region in which the first region is located; and generating medical service data according to the target quadrant position information and the lesion attribute.

Medical image processing method and apparatus, electronic medical device, and storage medium

A computer device, obtains a mammographic image of a unilateral breast. The mammographic image includes a cranial-caudal (CC)-position mammographic image and a mediolateral-oblique (MLO)-position mammographic image. The computer device invokes a breast detection model to perform a prediction of a condition of the unilateral breast according to the CC-position mammographic image and the MLO-position mammographic image. The device obtains a prediction result of the unilateral breast, and generates and outputs a detection report that includes the prediction result.

Mammographic image processing method and apparatus, system and medium

An image processing method performed by a terminal is provided. A molybdenum target image is obtained, and a plurality of candidate regions are extracted from the molybdenum target image. In the molybdenum target image, a target region is marked in the plurality of candidate regions by using a neural network model obtained by deep learning training, a probability that a lump comprised in the target region is a target lump being greater than a first threshold, a probability that the target lump is a malignant tumor being greater than a second threshold, and the neural network model being used for indicating a mapping relationship between a candidate region and a probability that a lump comprised in the candidate region is the target lump.

Image processing method and apparatus and neural network model training method

A door opening structure for microwave oven includes an upper hinge fastened on a door body assembly, and a lower hinge fastened on a oven body of a microwave oven, wherein the door body assembly is hinged with the oven body by the upper hinge and the lower hinge. An elastic element is provided between the upper hinge and the lower hinge, and the upper hinge is connected with a damper. Both ends of the upper hinge are provided with first connectors that are hinged with the lower hinge, and the middle portion of the upper hinge is provided with second connectors that are hinged with one end of the damper by spring pins.

Door opening structure for microwave oven

Growing community of inventors

Shenzhen, China

Cheng Jiang