Tencent Technology (shenzhen) Company Limited

Tencent Technology Company Limited

Yuexiang Li

Yefeng Zheng

Kai Ma

Yi Lin

Dong Wei

Donghuan Lu

Nanjun He

Hong Liu

Yifan Hu

Jiawei Chen

Liansheng Wang

Heng Guo

Xinrui Zhuang

Xinpeng Xie

An object recognition method includes extracting, by a first transformer network, spatial features of a plurality of medical images respectively, the plurality of medical images being images of a same object at different times, and fusing the extracted plurality of spatial features, to obtain a first fusion spatial feature of the object. The method further includes extracting, by a second transformer network, a spatial-temporal feature of the object based on the first fusion spatial feature. The spatial-temporal feature indicates a change in the spatial features of the plurality of medical images at the different times. The method further includes recognizing a state of the object based on the spatial-temporal feature, to obtain a recognition result of the object.

Object recognition using spatial and timing information of object images at diferent times

A computer device performs feature extraction on two-dimensional medical images included in a three-dimensional medical image, to obtain image features corresponding to the two-dimensional medical images. The three-dimensional medical image are obtained by continuously scanning a target tissue structure. The computer device determines offsets of the two-dimensional medical images in a target direction based on the image features. The computer device performs feature alignment on the image features based on the offsets, to obtain aligned image features. The computer device performs three-dimensional segmentation on the three-dimensional medical image based on the aligned image features, to obtain three-dimensional layer distribution of the target tissue structure in the three-dimensional medical image.

Method and apparatus for performing layer segmentation on tissue structure in medical image, device, and medium

A data processing method includes: acquiring an initial sample angiography image set; performing data expansion processing on a first sample angiography image based on physical characteristics of blood vessels at a target site to obtain a processed sample angiography image, performing label conversion processing on a first label based on the physical characteristics of the blood vessels at the target site to obtain a second label of the processed sample angiography image, and adding the processed sample angiography image and the second label to a target sample angiography image set; and training an angiography image recognition model using the initial sample angiography image set and the target sample angiography image set to obtain a trained angiography image recognition model. The performance of the trained angiography image recognition model is improved by increasing the number of samples.

Data processing method and apparatus, device and medium

An image classification method includes: performing image segmentation on an unlabeled sample image to obtain image blocks and performing feature extraction on each image block to obtain an initial image feature set including an initial image feature corresponding to each image block, rearranging and combining initial image features in the initial image feature set to obtain a first image feature set and a second image feature set, first image features in the first image feature set and second image features in the second image feature set corresponding to different rearrangement and combination manners, pre-training an image classification model based on the first image feature set and the second image feature set, the image classification model being configured to classify content in an image, and fine-tuning the pre-trained image classification model based on a labeled sample image.

Image classification method and apparatus, device, storage medium, and program product

An image processing method includes obtaining a sample image and a generative adversarial network (GAN), including a generation network and an adversarial network, and performing style conversion on the sample image, to obtain a reference image. The method further includes performing global style recognition on the reference image, to determine a global style loss between the reference image and the sample image, and performing image content recognition on the reference image and the sample image, to determine a content loss between the reference image and the sample image. The method also includes performing local style recognition on the reference image and the sample image, to determine a local style loss of the reference image and a local style loss of the sample image, training the generation network to obtain a trained generation network, and performing style conversion on a to-be-processed image by using the trained generation network.

Image processing method and apparatus, server, and storage medium

An image recognition method includes: obtaining a target three-dimensional (3D) image; inputting the target 3D image to a first recognition model; and obtaining the image type of the target 3D image outputted by the first recognition model. The first recognition model is configured to perform image recognition on the target 3D image to obtain an image type of the target 3D image. A convolutional block of the first recognition model is the same as a convolutional block of a second recognition model, and configured to perform image recognition on the target 3D image. The second recognition model is obtained by training an original recognition model using a target training sample, the target training sample including cubes obtained by rotating and sorting N target cubes obtained from a 3D sample image, N being a natural number greater than 1.

Image recognition method and apparatus, recognition model training method and apparatus, and storage medium

A method for determining a target image to be labeled includes: obtaining an original image and an autoencoder (AE) set, the original image being an image having not been labeled, the AE set including N AEs; obtaining an encoded image set corresponding to the original image by using the AE set, the encoded image set including N encoded images, the encoded images being corresponding to the AEs; obtaining the encoded image set and a segmentation result set corresponding to the original image by using an image segmentation network, the image segmentation network including M image segmentation sub-networks, and the segmentation result set including [(N+1)*M] segmentation results; determining labeling uncertainty corresponding to the original image according to the segmentation result set; and determining whether the original image is a target image according to the labeling uncertainty.

Method and apparatus for determining image to be labeled and model training method and apparatus

Embodiments of this application disclose a brain image segmentation method and apparatus, a network device, and a storage medium. The method includes obtaining, by a device, a to-be-segmented image group comprising a plurality of modal images of a brain. The device includes a memory storing instructions and a processor in communication with the memory. The method further includes performing, by the device, skull stripping according to the plurality of modal images to obtain a skull-stripped mask; separately performing, by the device, feature extraction on the plurality of modal images to obtain extracted features, and fusing the extracted features to obtain a fused feature; segmenting, by the device, encephalic tissues according to the fused feature to obtain an initial segmentation result; and fusing, by the device, the skull-stripped mask and the initial segmentation result to obtain a segmentation result corresponding to the to-be-segmented image group.

Growing community of inventors

Shenzhen, China

Yuexiang Li