Arizona State University

Md Mahfuzur Rahman Siddiquee

Jianming Liang

Nima Tajbakhsh

Zongwei Zhou

Ruibin Feng

Vatsal Sodha

Described herein are means for generating source models for transfer learning to application specific models used in the processing of medical imaging. In some embodiments, the method comprises: identifying a group of training samples, wherein each training sample in the group of training samples includes an image; for each training sample in the group of training samples: identifying an original patch of the image corresponding to the training sample; identifying one or more transformations to be applied to the original patch; generating a transformed patch by applying the one or more transformations to the identified patch; and training an encoder-decoder network using a group of transformed patches corresponding to the group of training samples, wherein the encoder-decoder network is trained to generate an approximation of the original patch from a corresponding transformed patch, and wherein the encoder-decoder network is trained to minimize a loss function that indicates a difference between the generated approximation of the original patch and the original patch. The source models significantly enhance the transfer learning performance for many medical imaging tasks including, but not limited to, disease/organ detection, classification, and segmentation. Other related embodiments are disclosed.

Systems, methods, and apparatuses for the generation of source models for transfer learning to application specific models used in the processing of medical imaging

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.

Systems, methods, and apparatuses for implementing fixed-point image-to-image translation using improved generative adversarial networks (GANs)

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 X, 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 X, a feature map x, wherein xis based on a convolution operation performed on a down-sampling of an output from Xwhen j=0, and wherein xis based on a convolution operation performed on an up-sampling operation of an output from Xwhen j&#x3e;0; and predicting a segmentation of a test image using the trained aggregate U-Net.

Methods, systems, and media for segmenting images

Methods, systems, and media for discriminating and generating translated images are provided. In some embodiments, the method comprises: identifying a set of training images, wherein each image is associated with at least one domain from a plurality of domains; training a generator network to generate: i) a first fake image that is associated with a first domain; and ii) a second fake image that is associated with a second domain; training a discriminator network, using as inputs to the discriminator network: i) an image from the set of training images; ii) the first fake image; and iii) the second fake image; and using the generator network to generate, for an image not included in the set of training images at least one of: i) a third fake image that is associated with the first domain; and ii) a fourth fake image that is associated with the second domain.

Methods, systems, and media for discriminating and generating translated images

Disclosed are provided systems, methods, and apparatuses for implementing a multi-resolution neural network for use with imaging intensive applications including medical imaging. For example, a system having means to execute a neural network model formed from a plurality of layer blocks including an encoder layer block which precedes a plurality of decoder layer blocks includes: means for associating a resolution value with each of the plurality of layer blocks; means for processing via the encoder layer block a respective layer block input including a down-sampled layer block output processing, via decoder layer blocks, a respective layer block input including an up-sampled layer block output and a layer block output of a previous layer block associated with a prior resolution value of a layer block which precedes the respective decoder layer block; and generating the respective layer block output by summing or concatenating the processed layer block inputs.

Systems, methods, and apparatuses for implementing a multi-resolution neural network for use with imaging intensive applications including medical imaging

Growing community of inventors

Tempe, AZ, United States of America

Md Mahfuzur Rahman Siddiquee