公开(公告)号：US12089915B2

公开(公告)日：2024-09-17

申请号：US18554680

申请日：2021-05-10

Applicant: HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Xuming Zhang ,  Tuo Wang

IPC: A61B5/055 ,  A61B5/00 ,  G06N3/084 ,  G06T7/00 ,  G06V10/764 ,  G06V10/82

CPC classification number: A61B5/004 ,  A61B5/055 ,  A61B5/7267 ,  G06N3/084 ,  G06T7/0012 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30081 ,  G06V10/764 ,  G06V10/82 ,  G06V2201/031

Abstract: The present invention discloses a method and a system for prostate multi-modal MR image classification based on a foveated residual network, the method comprising: replacing convolution kernels of a residual network using blur kernels in a foveation operator, thereby constructing a foveated residual network; training the foveated residual network using prostate multi-modal MR images having category labels, to obtain a trained foveated residual network; and classifying, using the foveated residual network, a prostate multi-modal MR image to be classified, so as to obtain a classification result. In the present invention, a foveation operator is designed based on human visual characteristics, blur kernels of the operator are extracted and used to replace convolution kernels in a residual network, thereby constructing a foveated deep learning network which can extract features that conform to the human visual characteristics, thereby improving the classification accuracy of prostate multi-modal MR images.