Tencent Technology (Shenzhen) Company Limited

Huawei Technologies Co., Limited

Tencent Technology Company Limited

Han Zheng

Hong Shang

Zhongqian Sun

Xinghui Fu

Xiaofeng Zhang

Jianhua Yao

Xiaoning Wang

Junwen Qiu

Wen Zhang

Wei Yang

Rui Li

Yongfu Sun

Zhigang Hao

Baiwan Zhu

Lishu Luo

Jiaxuan Zhuo

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

An object detection model training method includes: inputting an unannotated first sample image into an initial detection model of a current round, and outputting a first prediction result for a target object, transforming the first sample image and a first prediction position region within the first prediction result to obtain a second sample image and a prediction transformation result in the second sample image; inputting the second sample image into the initial detection model, and outputting a second prediction result for the target object; obtaining a loss value of unsupervised learning according to a difference between the second prediction result and the prediction transformation result; and adjusting model parameters of the initial detection model according to the loss value and returning to the operation of inputting a first sample image into an initial detection model of a current round to perform iterative training, to obtain an object detection model.

Object detection model training method and apparatus, object detection method and apparatus, computer device, and storage medium

A method for training an image recognition model includes: obtaining training image sets; obtaining a first predicted probability, a second predicted probability, a third predicted probability, and a fourth predicted probability based on the training image sets by using an initial image recognition model; determining a target loss function according to the first predicted probability, the second predicted probability, the third predicted probability, and the fourth predicted probability; and training the initial image recognition model based on the target loss function, to obtain an image recognition model.

Method and apparatus for training image recognition model, and image recognition method and apparatus

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

This application relates to an endoscopic image display method, apparatus, computer device, and storage medium, and relates to the field of machine learning technologies. The method acquiring an endoscopic image; locating a target region image in the endoscopic image, the target region image being a partial image comprising a target region; inputting the target region image into a coding network to obtain a semantic feature of the target region image, the coding network being a part of an image classification network, and the image classification network being a machine learning network obtained through training with first training images; matching the semantic feature of the target region image against semantic features of image samples to obtain a matching result, the matching result indicating a target image sample that matches the target region image; and displaying the endoscopic image and the matching result in an endoscopic image display interface.

Endoscopic image display method, apparatus, computer device, and storage medium

A medical image processing method includes: determining a target mask of a target object in a medical image and a reference mask of a reference object in the medical image, the target mask indicating a position and a boundary of the target object, and the reference mask indicating a position and a boundary of the reference object; determining a feature size of the target object based on the target mask; determining a feature size of the reference object based on the reference mask; and determining a target size of the target object based on the feature size of the reference object, a preset mapping relationship between the feature size of the reference object and a reference size, and the feature size of the target object.

Medical image processing method, apparatus, and device, medium, and endoscope

Embodiments of this application disclose an image processing method performed by a computer device, and a computer-readable storage medium. The method includes: obtaining a to-be-detected image, and performing down-sampling abnormality classification processing on the to-be-detected image, to obtain a predicted abnormality category label and a target feature image; performing preliminary abnormality positioning processing based on the predicted abnormality category label and the target feature image, to obtain an initial positioning image corresponding to the to-be-detected image; performing up-sampling abnormality positioning processing on the initial positioning image, to obtain a target positioning image corresponding to the to-be-detected image; and outputting the predicted abnormality category label and the target positioning image, the initial positioning image and the target positioning image being configured for reflecting attribute information of a target region associated with the predicted abnormality category label within the to-be-detected image.

Image processing method and apparatus, server, medical image processing device and storage medium

This application relates to an image recognition model training method, an image recognition method, apparatus, and system. The method includes: obtaining a to-be-recognized image; extracting image feature information of the to-be-recognized image; and obtaining a lesion category recognition result of the to-be-recognized image by using the image feature information of the to-be-recognized image as an input parameter of a preset image recognition model, the image recognition model being trained by using a training image sample set comprising at least one strong-label training image sample, to determine the lesion category recognition result; and the strong-label training image sample representing an image sample having strong-label information, and the strong-label information comprising at least annotation information of a lesion category and a lesion position in the strong-label training image sample. According to the lesion position, image feature information of a specific lesion category may be more accurately positioned, thereby improving reliability and accuracy.

Image recognition model training method and apparatus, and image recognition method, apparatus, and system

A data disaster tolerance method, device and system is disclosed. Each node in a logic unit including a single master node and two or more slave nodes is monitored. If the master node is abnormal, the server acquires log information of the plurality of two or more slave nodes separately, the log information of the two or more slave nodes includes respective time points of data synchronization between the slave nodes and the master node A respective slave node of the two or more slave nodes having the time point of data synchronization closest to a current time is selected as a target node. A master-slave relationship in the logic unit is updated to change a role of the target node to that of the master node.

Data disaster recovery method, device and system

A method for managing a processor includes: obtaining an online request of a processor of a computer system; collecting lock contention information of the computer system if a lock contention status flag indicates a non-lock thrashing status; determining whether the computer system is in a lock thrashing status according to the lock contention information; and accepting the online request if it is determined that the computer system is in a non-lock thrashing status. By using the management method according to embodiments of the present application, processor performance degradation and a waste of idle processor resources that are caused by the case that the computer system is in a lock thrashing status are prevented, thereby improving utilization efficiency of processor resources and promoting overall performance of the computer system.

Method for managing a processor, lock contention management apparatus, and computer system

A method and a device for balancing load of a multiprocessor system relate to the resource allocation field of the multiprocessor system, for achieving the object of reducing the number of accessing a remote node memory or the amount of copying data when the processes migrated into a target Central Processing Unit (CPU) are performed. The method for balancing the load of the multiprocessor system includes: determining the local CPU and the target CPU in the multiprocessor system; sequencing the migration priorities, based on the size of memory space occupied by the processes in the queue of the local CPU; where the less the memory space occupied by the process is, the higher the migration priority of the process is; and migrating the process whose migration priority is the highest other than the processes being performed in the queue of the local CPU into the target CPU.

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Shenzhen, China

Han Zheng