公开(公告)号：US11756161B2

公开(公告)日：2023-09-12

申请号：US17340117

申请日：2021-06-07

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Zhanli Hu ,  Hairong Zheng ,  Na Zhang ,  Xin Liu ,  Dong Liang ,  Yongfeng Yang ,  Hanyu Sun

IPC: G06T5/00 ,  A61B6/03 ,  G06N3/08 ,  G06N3/045

CPC classification number: G06T5/00 ,  A61B6/037 ,  G06N3/045 ,  G06N3/08 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/20081

Abstract: The present application relates to a method and system for generating multi-task learning-type generative adversarial network for low-dose PET reconstruction, and relates to the field of deep learning. The method includes connecting layers of the encoder with layers of the decoder by skip connection to provide a U-Net type picture generator; generating a group of generative adversarial networks by matching a plurality of picture generators with a plurality of discriminators in one-to-one manner; obtaining a first multi-task learning-type generative adversarial network; designing a joint loss function 1 for improving image quality; and training the first multi-task learning-type generative adversarial network according to the joint loss function 1 in combination with an optimizer to provide a second multi-task learning-type generative adversarial network.

公开(公告)号：US11210783B2

公开(公告)日：2021-12-28

申请号：US16910074

申请日：2020-06-24

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Hairong Zheng ,  Xin Liu ,  Na Zhang ,  Zhanli Hu ,  Dong Liang ,  Yongfeng Yang

IPC: G06K9/00 ,  G06T7/00 ,  G16H30/40 ,  G06K9/62 ,  G06N3/08 ,  G06T11/00

Abstract: A method of processing plaques in magnetic resonance imaging of vessel wall include: step S101, training a generative adversarial network and a capsule neural network to obtain a trained generator network and a trained capsule neural network; and step S102, cascade-connecting the trained generator network with the capsule neural network into a system to recognize and classify plaques in magnetic resonance imaging of vessel wall. In one aspect, the capsule neural network has more abundant vascular plaques characteristic information represented by vector; in another aspect, when the trained generator network and the capsule neural network are cascaded into the system to recognize and classify the plaques in magnetic resonance imaging of vessel wall, an accuracy of recognition and classification may be greatly improved. A device for processing the method as well as a computer for implementing are also disclosed.

公开(公告)号：US12224057B2

公开(公告)日：2025-02-11

申请号：US17739121

申请日：2022-05-08

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Na Zhang ,  Hairong Zheng ,  Xin Liu ,  Zhanli Hu ,  Zhiyuan Huang ,  Dong Liang

IPC: G16H30/00 ,  G06N3/084 ,  G06T5/70 ,  G06V10/75 ,  G06V10/774 ,  G16H30/40

Abstract: A medical image processing method and processing apparatus, and a computer readable storage medium. The method includes: obtaining a to-be-processed image; performing a feature extraction on the to-be-processed image to obtain a corresponding feature image; and re-determining a pixel value of each pixel in the to-be-processed image based on first information and second information of a corresponding pixel in the feature image, and processing the to-be-processed image; wherein the first information is information of a pixel adjacent to the corresponding pixel in the features image, and the second information is information of a pixel that is not adjacent to and is similar to the corresponding pixel in the features image.

公开(公告)号：US12190568B2

公开(公告)日：2025-01-07

申请号：US17889189

申请日：2022-08-16

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY

Inventor： Hairong Zheng ,  Xin Liu ,  Na Zhang ,  Zhanli Hu ,  Qihang Chen ,  Dong Liang ,  Yongfeng Yang

IPC: G06V10/774 ,  A61B5/055 ,  G06T11/00 ,  G06V10/42

Abstract: Methods and apparatuses for training a magnetic resonance imaging model, electronic devices and computer readable storage media are provided. A method may include: acquiring a magnetic resonance image data set; constructing a ring deep neural network to be trained; inputting an under-sampled magnetic resonance image and a full-sampled magnetic resonance image respectively to two neural networks included in the ring deep neural network, to generate respective simulated magnetic resonance images; inputting a first simulated full-sampled magnetic resonance image and the full-sampled magnetic resonance image to a pre-constructed first simulated magnetic resonance image class discrimination model, to obtain a first discrimination result indicating whether or not the first simulated full-sampled magnetic resonance image is of a simulated magnetic resonance image class; and adjusting a network parameter of the ring deep neural network based on a preset loss function, to obtain a trained magnetic resonance imaging model.