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公开(公告)号：US11164021B2
公开(公告)日：2021-11-02
申请号：US16875680
申请日：2020-05-15
Inventor： Md Mahfuzur Rahman Siddiquee , Zongwei Zhou , Ruibin Feng , Nima Tajbakhsh , Jianming Liang
Abstract: 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.
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公开(公告)号：US12260622B2
公开(公告)日：2025-03-25
申请号：US17625313
申请日：2020-07-17
Inventor： Zongwei Zhou , Vatsal Sodha , Md Mahfuzur Rahman Siddiquee , Ruibin Feng , Nima Tajbakhsh , Jianming Liang
IPC: G06V10/82 , G06V10/774 , G06V10/776 , G06V10/98
Abstract: 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.
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Patent Agency Ranking
公开(公告)号：US20220114733A1
公开(公告)日：2022-04-14
申请号：US17497528
申请日：2021-10-08
Inventor： Ruibin Feng , Zongwei Zhou , Jianming Liang
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.
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公开(公告)号：US20230116897A1
公开(公告)日：2023-04-13
申请号：US17961896
申请日：2022-10-07
Inventor： Mohammad Reza Hosseinzadeh Taher , Fatemeh Haghighi , Ruibin Feng , Jianming Liang
IPC: G16H50/20 , G06T7/00 , G06V10/774 , G06V10/82
Abstract: Described herein are means for implementing systematic benchmarking analysis to improve transfer learning for medical image analysis. An exemplary system is configured with specialized instructions to cause the system to perform operations including: receiving training data having a plurality medical images therein; iteratively transforming a medical image from the training data into a transformed image by executing instructions for resizing and cropping each respective medical image from the training data to form a plurality of transformed images; applying data augmentation operations to the transformed images; applying segmentation operations to the augmented images; pre-training an AI model on different input images which are not included in the training data by executing self-supervised learning for the AI model; fine-tuning the pre-trained AI model to generate a pre-trained diagnosis and detection AI model; applying the pre-trained diagnosis and detection AI model to a new medical image to render a prediction as to the presence or absence of a disease within the new medical image; and outputting the prediction as a predictive medical diagnosis for a medical patient.
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公开(公告)号：US20220262105A1
公开(公告)日：2022-08-18
申请号：US17625313
申请日：2020-07-17
Applicant: Zongwei ZHOU , Vatsal SODHA , Md, Mahfuzur RAHMAN SIDDIQUEE , Ruibin FENG , Nima TAJBAKHSH , Jianming LIANG , Arizona Board of Regents on behalf of Arizona State University
Inventor： Zongwei Zhou , Vatsal Sodha , Md Mahfuzur Rahman Siddiquee , Ruibin Feng , Nima Tajbakhsh , Jianming Liang
IPC: G06V10/774 , G06V10/82 , G06V10/98 , G06V10/776
Abstract: 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.
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公开(公告)号：US20210342646A1
公开(公告)日：2021-11-04
申请号：US17240271
申请日：2021-04-26
Inventor： Ruibin Feng , Zongwei Zhou , Jianming Liang
Abstract: Described herein are means for training a deep model to learn contrastive representations embedded within part-whole semantics via 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 specifically 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 resized 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.
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公开(公告)号：US11915417B2
公开(公告)日：2024-02-27
申请号：US17240271
申请日：2021-04-26
Inventor： Ruibin Feng , Zongwei Zhou , Jianming Liang
CPC classification number: G06T7/0012 , G06F18/2155 , G06T7/174 , G06T15/08 , G06T17/10 , G06V10/82 , G06T2207/20081 , G06T2207/20084 , G06T2207/20132 , G06T2207/30016 , G06T2207/30056 , G06V2201/031
Abstract: Described herein are means for training a deep model to learn contrastive representations embedded within part-whole semantics via 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 specifically 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 resized 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.
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