Tencent Technology (Shenzhen) Company Limited

Tencent Technology Company Limited

Jianhua Yao

Liang Wang

Xiao Han

Jiarui Sun

Jun Liao

Yanchun Zhu

Tao Li

Hong Shang

Han Zheng

Cheng Jiang

Kuan Tian

Kezhou Yan

Niyun Zhou

Rongbo Shen

Xiaoning Wang

Yan Zhang

Fan Yang

Junzhou Huang

Kaiwen Xiao

Weijia Lu

De Cai

Jia Rui Sun

Zongbo Han

Yen-Hsiang Wang

Changqing Zhang

Hanbo Chen

Yuqi Fang

Yan Chun Zhu

Jiangpeng Yan

Yixun Liu

Chenchen Qin

The present subject matter relates to the field of machine learning and provides a classification and prediction method and apparatus, a device, a storage medium, and a computer program product. At least two pieces of data are acquired for a specified classification task. Each piece of data corresponds to one modality. A confidence corresponding to each of at least two modalities is acquired. The confidence indicates a classification and prediction probability of the modality in the specified classification task. Weighted fusion on data features of the at least two pieces of data is performed based on the confidence corresponding to each of the at least two modalities to obtain a fused feature. A prediction is performed according to the fused feature to obtain a classification and prediction result corresponding to the specified classification task.

Classification and prediction method and apparatus, device, storage medium, and computer program product

This application provides an image processing method and apparatus that can: acquire a target object through each narrow-band filter to obtain a narrow-band channel image including the target object; and fuse multiple narrow-band channel images in one-to-one correspondence with the multiple narrow-band filters to obtain a color image including a contour of the target object.

Method and apparatus, electronic device, computer-readable storage medium, and computer program product for obtaining color image based on fusing narrow band images

This application provides an artificial intelligence-based pathological image processing method performed by an electronic device. The method includes: determining a seed pixel of an immune cell region from a pathological image; obtaining a seed pixel mask image corresponding to the seed pixel of the immune cell region from the pathological image based on the seed pixel of the immune cell region; segmenting an epithelial cell region in the pathological image, to obtain an epithelial cell mask image of the pathological image; fusing the seed pixel mask image and the epithelial cell mask image of the pathological image, to obtain an effective seed pixel mask image corresponding to the immune cell region in the pathological image; and determining a ratio value of the immune cell region in the pathological image based on the effective seed pixel mask image.

Artificial intelligence-based pathological image processing method and apparatus, electronic device, and storage medium

An image analysis method includes: obtaining an image; performing image classification on the image by using an image classification network, to obtain an image classification result of an image category of the image, the image category including a first category and a second category different from the first category; performing object detection on the image by using an object detection network, to obtain an object detection result of a target object associated with the first category; and generating an image analysis result of the image based on the image classification result and the object detection result.

Image analysis method, image analysis apparatus, and image analysis system

This application discloses an image clustering method performed by a computer device. This method includes: determining a first clustering parameter based on M image groups; for a target image group, dividing it into two image groups to obtain M+1 reference image groups, and determining a reference clustering parameter based on the M+1 reference image groups as a second clustering parameter for the target image group representing a clustering degree of images in the M+1 reference image groups; choosing, among respective second clustering parameters of the M image groups, a target second clustering parameter representing the highest clustering degree of images in the M image groups; and when the target second clustering parameter has a clustering degree not lower than the first clustering parameter, dividing a target image group corresponding to the target second clustering parameter into two image groups to obtain M+1 image groups.

Image clustering method and apparatus, computer device, and storage medium

A computer device obtains a confocal microscopy image. The device determines a pinhole diameter of a first detector pinhole of a confocal microscope that is used to acquire the image. The pinhole diameter has an influence on a parameter of the image. The device obtains a target image processing model corresponding to the pinhole diameter of the first detector pinhole. The target image processing model is configured to improve the parameter of the image. The device causes the target image processing model to process the image to obtain a target image having the improved parameter.

Artificial intelligence-based image processing method and apparatus, device, and medium

An artificial intelligence-based image processing method implemented by a computer device is provided. The method includes: acquiring an image; performing element region detection on the image to determine an element region in the image; detecting a target element region in the image using an artificial intelligence-based technique; generating a target element envelope region by searching an envelope for the detected target element region; and fusing the element region and the target element envelope region to obtain a target element region outline.

Artificial intelligence-based image processing method and apparatus, computer device and storage medium

A computer device obtains a medical image set. The device identifies a difference between the reference medical image and the target medical image to obtain a candidate non-lesion region in the target medical image. The device determines area size information of the candidate non-lesion region as candidate area size information. The device adjusts the candidate non-lesion region according to the annotated area size information when the candidate area size information does not match the annotated area size information, so as to obtain a target non-lesion region in the target medical image.

Medical image processing method and apparatus, device, storage medium, and product

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 medical image processing method and apparatus, and an image processing method and apparatus, terminal and storage medium that obtains a to-be-processed medical image; generates a difference image according to the first image data, the second image data, and the third image data included in the to-be-processed medical image; and performs binarization processing on the difference image to obtain a binarized image, a foreground region of the binarized image corresponding to a pathological tissue region of the to-be-processed medical image. A difference image is generated based on color information of different channels before binarization processing is performed on an image, thereby effectively using the color information in the image. The pathological tissue region extracted based on the difference image is more accurate and facilitates subsequent image analysis.

Medical image processing method and apparatus, image processing method and apparatus, terminal and storage medium

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

This disclosure discloses a microscope system, a smart medical device, an automatic focusing method, and a storage medium. The smart medical device includes an objective lens, a beam splitter, an image projector assembly, a camera assembly, and a focusing device. The objective lens includes a first end and a second end, and the first end faces a to-be-observed sample. The beam splitter is disposed on the second end. The image projector assembly is in communication with the beam splitter, the image projector assembly includes a first lens and an image projection device, and light generated by the image projector assembly enters the beam splitter through the first lens. The camera assembly includes a camera. The focusing device is disposed on the camera assembly, and the focusing device is configured to perform focus adjustment on the camera.

Microscope system, smart medical device, automatic focusing method and storage medium

An artificial intelligence-based image processing method includes: obtaining a first sample image of a source domain and a second sample image of a target domain, the first sample image of the source domain carrying a corresponding target processing result; converting the first sample image into a target sample image, the target sample image carrying a corresponding target processing result; training a first image processing model based on the target sample image and the target processing result corresponding to the target sample image, to obtain a second image processing model; and inputting, in response to obtaining a human tissue image of the target domain, the human tissue image into the second image processing model, positioning, by the second image processing model, a target human tissue in the human tissue image, and outputting position information of the target human tissue in the human tissue image.

Artificial intelligence-based image processing method and apparatus, device, and storage medium

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

This application discloses an artificial intelligence-based image processing method, apparatus, device, and storage medium, and relates to the field of computer technology. The method includes: obtaining a slice image; dividing the slice image to obtain a plurality of image blocks; feeding the plurality of image blocks into a labeling model, extracting, by the labeling model, a pixel feature of the slice image based on the plurality of image blocks, determining a plurality of vertex positions of a polygonal region in the slice image based on the pixel feature, concatenating the plurality of vertex positions, and outputting label information of the slice image, the polygonal region being a region in which a target pathological tissue of interest is located.

Growing community of inventors

Shenzhen, China

Jianhua Yao