Tencent Technology (shenzhen) Company Limited

Tencent Technology Company Limited

Jia Chang

Xuan Zhou

Yi Cheng Zhang

Xiaowei Guo

Xinyang Jiang

Yi Zhang

Fen Xiao

Jian Zhu

Cheng Jiang

Yan Liu

Kuan Tian

Yanchun Zhu

Ke Yan

Xing Sun

Hang Ruan

Jianrong Wu

Zheng Gao

Xiaohui Han

A labeling method is provided. In the method, target image data of a target image is acquired from an offline storage device. The target image data is encrypted based on a first target account. A permission to load the target image data is verified based on a second target account logged in to a labeling client. The target image is loaded in the labeling client in response to the permission to load the target image being verified when the second target account logged in to the labeling client matches the first target account. A label for the target image is received. Further, the label for the target image is transmitted to a server. The server is configured to store the label in association with the target image. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.

Labeling method, apparatus, and device, and readable storage 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

This application provides an image recognition method, a storage medium, and a computer device. The method includes: obtaining a to-be-recognized image; preprocessing the to-be-recognized image, to obtain a preprocessed image; obtaining, through a first submodel in a machine learning model, a first image feature corresponding to the to-be-recognized image, and obtaining, through a second submodel in the machine learning model, a second image feature corresponding to the preprocessed image; and determining, according to the first image feature and the second image feature, a first probability that the to-be-recognized image belongs to a classification category corresponding to the machine learning model. It may be seen that, the solutions provided by this application can improve recognition efficiency and accuracy.

Image recognition method, storage medium and computer device

Growing community of inventors

Shenzhen, China

Jia Chang