Tencent Technology (Shenzhen) Company Limited

Tencent Technology Company Limited

Kuan Tian

Cheng Jiang

Tao Li

Kezhou Yan

Jianhua Yao

Rongbo Shen

Xiao Han

Liang Wang

Xuan Zhou

Fen Xiao

Jian Zhu

Ke Yan

Ke Zhou

Kaiwen Xiao

Jia Chang

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

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

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

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.

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Shenzhen, China

Kuan Tian