公开(公告)号：US12229920B2

公开(公告)日：2025-02-18

申请号：US17477088

申请日：2021-09-16

Applicant: Arizona Board of Regents on behalf of Arizona State University

Inventor： Jianming Liang ,  Zongwei Zhou ,  Nima Tajbakhsh ,  Md Mahfuzur Rahman Siddiquee

IPC: G06T5/50 ,  G06T7/00

Abstract: Described herein are means for implementing fixed-point image-to-image translation using improved Generative Adversarial Networks (GANs). For instance, an exemplary system is specially configured for implementing a new framework, called a Fixed-Point GAN, which improves upon prior known methodologies by enhancing the quality of the images generated through global, local, and identity transformation. The Fixed-Point GAN as introduced and described herein, improves many applications dependant on image-to-image translation, including those in the field of medical image processing for the purposes of disease detection and localization. Other related embodiments are disclosed.

公开(公告)号：US11922628B2

公开(公告)日：2024-03-05

申请号：US17224886

申请日：2021-04-07

Applicant: Arizona Board of Regents on Behalf of Arizona State University

Inventor： Zongwei Zhou ,  Vatsal Sodha ,  Jiaxuan Pang ,  Jianming Liang

IPC: G06V10/00 ,  G06F18/21 ,  G06F18/214 ,  G06N3/045 ,  G06N3/088 ,  G06T3/00 ,  G06T7/11 ,  G06V10/26 ,  G06V10/77 ,  G16H30/40

CPC classification number: G06T7/11 ,  G06F18/2155 ,  G06F18/2163 ,  G06N3/045 ,  G06N3/088 ,  G06T3/00 ,  G06V10/26 ,  G06V10/7715 ,  G16H30/40 ,  G06V2201/03

Abstract: Described herein are means for generation of self-taught generic models, named Models Genesis, without requiring any manual labeling, in which the Models Genesis are then utilized for the processing of medical imaging. For instance, an exemplary system is specially configured for learning general-purpose image representations by recovering original sub-volumes of 3D input images from transformed 3D images. Such a system operates by cropping a sub-volume from each 3D input image; performing image transformations upon each of the sub-volumes cropped from the 3D input images to generate transformed sub-volumes; and training an encoder-decoder architecture with skip connections to learn a common image representation by restoring the original sub-volumes cropped from the 3D input images from the transformed sub-volumes generated via the image transformations. A pre-trained 3D generic model is thus provided, based on the trained encoder-decoder architecture having learned the common image representation which is capable of identifying anatomical patterns in never before seen 3D medical images having no labeling and no annotation. More importantly, the pre-trained generic models lead to improved performance in multiple target tasks, effective across diseases, organs, datasets, and modalities.

公开(公告)号：US11328430B2

公开(公告)日：2022-05-10

申请号：US16885579

申请日：2020-05-28

Applicant: Arizona Board of Regents on behalf of Arizona State University

Inventor： Zongwei Zhou ,  Md Mahfuzur Rahman Siddiquee ,  Nima Tajbakhsh ,  Jianming Liang

IPC: G06K9/00 ,  G06T7/143 ,  G06T7/00

Abstract: Methods, systems, and media for segmenting images are provided. In some embodiments, the method comprises: generating an aggregate U-Net comprised of a plurality of U-Nets, wherein each U-Net in the plurality of U-Nets has a different depth, wherein each U-Net is comprised of a plurality of nodes Xi,j, wherein i indicates a down-sampling layer the U-Net, and wherein j indicates a convolution layer of the U-Net; training the aggregate U-Net by: for each training sample in a group of training samples, calculating, for each node in the plurality of nodes Xi,j, a feature map xi,j, wherein xi,j is based on a convolution operation performed on a down-sampling of an output from Xi−1,j when j=0, and wherein xi,j is based on a convolution operation performed on an up-sampling operation of an output from Xi+1,j−1 when j>0; and predicting a segmentation of a test image using the trained aggregate U-Net.

公开(公告)号：US20220114733A1

公开(公告)日：2022-04-14

申请号：US17497528

申请日：2021-10-08

Applicant: Arizona Board of Regents on behalf of Arizona State University

Inventor： Ruibin Feng ,  Zongwei Zhou ,  Jianming Liang

IPC: G06T7/11 ,  G06T7/00 ,  G06N3/08

Abstract: Described herein are means for implementing contrastive learning via reconstruction within a self-supervised learning framework, in which the trained deep models are then utilized for the processing of medical imaging. For instance, an exemplary system is specially configured for performing a random cropping operation to crop a 3D cube from each of a plurality of medical images received at the system as input; performing a resize operation of the cropped 3D cubes; performing an image reconstruction operation of the resized and cropped 3D cubes to predict the whole image represented by the original medical images received; and generating a reconstructed image which is analyzed for reconstruction loss against the original image representing a known ground truth image to the reconstruction loss function. Other related embodiments are disclosed.