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公开(公告)号：US11922628B2
公开(公告)日：2024-03-05
申请号：US17224886
申请日：2021-04-07
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.
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22.发明申请公开(公告)号：US20220270357A1
公开(公告)日：2022-08-25
申请号：US17675929
申请日：2022-02-18
Inventor： Diksha Goyal , Jianming Liang
IPC: G06V10/778 , G06T7/194 , G06V10/82 , G06V10/774 , G06T7/00 , G06T7/11
Abstract: Described herein are means for implementing medical image segmentation using interactive refinement, in which the trained deep models are then utilized for the processing of medical imaging. For instance, an exemplary system is specially configured for operating a two-step deep learning training framework including means for receiving original input images at the deep learning training framework; means for generating an initial prediction image specifying image segmentation by processing the original input images through the base segmentation model to render the initial prediction image in the absence of user input guidance signals; means for receiving user input guidance signals indicating user-guided segmentation refinements to the initial prediction image; means for routing each of (i) the original input images, (ii) the initial prediction image, and (iii) the user input guidance signals to an InterCNN; means for generating a refined prediction image specifying refined image segmentation by processing each of the (i) the original input images, (ii) the initial prediction image, and (iii) the user input guidance signals through the InterCNN to render the refined prediction image incorporating the user input guidance signals; and means for outputting a refined segmentation mask based on application of the user input guidance signals to the deep learning training framework as a guidance signal. Other related embodiments are disclosed.
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公开(公告)号：US11328430B2
公开(公告)日：2022-05-10
申请号：US16885579
申请日：2020-05-28
Inventor： Zongwei Zhou , Md Mahfuzur Rahman Siddiquee , Nima Tajbakhsh , Jianming Liang
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.
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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：US20210150710A1
公开(公告)日：2021-05-20
申请号：US17098422
申请日：2020-11-15
Inventor： Mohammad Reza Hosseinzadeh Taher , Fatemeh Haghighi , Jianming Liang
Abstract: Not only is annotating medical images tedious and time consuming, but it also demands costly, specialty-oriented expertise, which is not easily accessible. To address this challenge, a new self-supervised framework is introduced: TransVW (transferable visual words), exploiting the prowess of transfer learning with convolutional neural networks and the unsupervised nature of visual word extraction with bags of visual words, resulting in an annotation-efficient solution to medical image analysis. TransVW was evaluated using NIH ChestX-ray14 to demonstrate its annotation efficiency. When compared with training from scratch and ImageNet-based transfer learning, TransVW reduces the annotation efforts by 75% and 12%, respectively, in addition to significantly accelerating the convergence speed. More importantly, TransVW sets new records: achieving the best average AUC on all 14 diseases, the best individual AUC scores on 10 diseases, and the second best individual AUC scores on 3 diseases. This performance is unprecedented, because heretofore no self-supervised learning method has outperformed ImageNet-based transfer learning and no annotation reduction has been reported for self-supervised learning. These achievements are contributable to a simple yet powerful observation: The complex and recurring anatomical structures in medical images are natural visual words, which can be automatically extracted, serving as strong yet free supervision signals for CNNs to learn generalizable and transferable image representation via self-supervision.
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Patent Agency Ranking
公开(公告)号：US12236592B2
公开(公告)日：2025-02-25
申请号：US17944881
申请日：2022-09-14
Inventor： Nahid Ul Islam , Shiv Gehlot , Zongwei Zhou , Jianming Liang
Abstract: Described herein are means for systematically determining an optimal approach for the computer-aided diagnosis of a pulmonary embolism, in the context of processing medical imaging. According to a particular embodiment, there is a system specially configured for diagnosing a Pulmonary Embolism (PE) within new medical images which form no part of the dataset upon which the AI model was trained. Such a system executes operations for receiving a plurality of medical images and processing the plurality of medical images by executing an image-level classification algorithm to determine the presence or absence of a Pulmonary Embolism (PE) within each image via operations including: pre-training an AI model through supervised learning to identify ground truth; fine-tuning the pre-trained AI model specifically for PE diagnosis to generate a pre-trained PE diagnosis and detection AI model; wherein the pre-trained AI model is based on a modified CNN architecture having introduced therein a squeeze and excitation (SE) block enabling the CNN architecture to extract informative features from the plurality of medical images by fusing spatial and channel-wise information; applying the pre-trained PE diagnosis and detection AI model to new medical images to render a prediction as to the presence or absence of the Pulmonary Embolism within the new medical images; and outputting the prediction as a PE diagnosis for a medical patient.
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公开(公告)号：US20240290076A1
公开(公告)日：2024-08-29
申请号：US18528675
申请日：2023-12-04
Inventor： Mohammad Reza Hosseinzadeh Taher , Jianming Liang
IPC: G06V10/774 , G06V10/82 , G06V20/50 , G06V40/10 , G16H30/40
CPC classification number: G06V10/774 , G06V10/82 , G06V20/50 , G06V40/10 , G16H30/40 , G06V2201/03
Abstract: Systems, methods, and apparatuses for learning foundation models from anatomy in medical imaging for use with medical image classification and/or image segmentation in the context of medical image analysis. Exemplary systems include means for receiving medical images; extracting human anatomical patterns from the medical images; generating a foundation model via learning the human anatomical patterns from within the medical images received, resulting in generic representations of the human anatomical patterns; wherein the learning includes: first learning prominent objects from within the medical images received corresponding to the human anatomical patterns; and secondly learning detailed parts within the learned prominent objects corresponding to sub-portions of the generic representations of the human anatomical patterns; wherein the learning further includes executing a self-supervised contrastive learning framework, including: executing an anatomy decomposer (AD) of the self-supervised contrastive learning framework which guides the generated foundation model to conserve hierarchical relationships of anatomical structures within the medical images received; and executing a purposive pruner (PP) of the self-supervised contrastive learning framework which forces the model to capture more distinct representations for different anatomical structures at varying granularity levels; and outputting the generated foundation model for use in processing medical images which form no part of the medical images received and used for training the generated foundation model.
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公开(公告)号：US20240078666A1
公开(公告)日：2024-03-07
申请号：US18241809
申请日：2023-09-01
Inventor： Jiaxuan PANG , Fatemeh Haghighi , DongAo Ma , Nahid Ui Islam , Mohammad Reza Hosseinzadeh Taher , Jianming Liang
CPC classification number: G06T7/0012 , G06T7/11 , G06V10/54 , G16H30/40 , G06T2207/20081 , G06V2201/03
Abstract: A self-supervised machine learning method and system for learning visual representations in medical images. The system receives a plurality of medical images of similar anatomy, divides each of the plurality of medical images into its own sequence of non-overlapping patches, wherein a unique portion of each medical image appears in each patch in the sequence of non-overlapping patches. The system then randomizes the sequence of non-overlapping patches for each of the plurality of medical images, and randomly distorts the unique portion of each medical image that appears in each patch in the sequence of non-overlapping patches for each of the plurality of medical images. Thereafter, the system learns, via a vision transformer network, patch-wise high-level contextual features in the plurality of medical images, and simultaneously, learns, via the vision transformer network, fine-grained features embedded in the plurality of medical images.
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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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公开(公告)号：US20230281805A1
公开(公告)日：2023-09-07
申请号：US18111136
申请日：2023-02-17
Inventor： Fatemeh Haghighi , Mohammad Reza Hosseinzadeh Taher , Jianming Liang
CPC classification number: G06T7/0012 , G06T5/002 , G06V10/761 , G06V10/762 , G16H30/40 , G16H50/20 , G06T2207/20081 , G06T2207/20132 , G06T2207/30096
Abstract: A Discriminative, Restorative, and Adversarial (DiRA) learning framework for self-supervised medical image analysis is described. For instance, a pre-trained DiRA framework may be applied to diagnosis and detection of new medical images which form no part of the training data. The exemplary DiRA framework includes means for receiving training data having medical images therein and applying discriminative learning, restorative learning, and adversarial learning via the DiRA framework by cropping patches from the medical images; inputting the cropped patches to the discriminative and restorative learning branches to generate discriminative latent features and synthesized images from each; and applying adversarial learning by executing an adversarial discriminator to perform a min-max function for distinguishing the synthesized restorative image from real medical images. The pre-trained model of the DiRA framework is then provided as output for use in generating predictions of disease within medical images.
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