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    SYSTEMS, METHODS, AND APPARATUSES FOR THE GENERATION OF SELF-TAUGHT MODELS GENESIS ABSENT MANUAL LABELING FOR THE PROCESSING OF MEDICAL IMAGING
    12.
    发明申请
    SYSTEMS, METHODS, AND APPARATUSES FOR THE GENERATION OF SELF-TAUGHT MODELS GENESIS ABSENT MANUAL LABELING FOR THE PROCESSING OF MEDICAL IMAGING 有权

    公开(公告)号：US20210326653A1

    公开(公告)日：2021-10-21

    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: G06K9/62 ,  G06T3/00 ,  G06N3/04 ,  G06N3/08 ,  G16H30/40

    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.

    Systems, methods, and apparatuses for actively and continually fine-tuning convolutional neural networks to reduce annotation requirements
    13.
    发明授权
    Systems, methods, and apparatuses for actively and continually fine-tuning convolutional neural networks to reduce annotation requirements 有权

    公开(公告)号：US12216737B2

    公开(公告)日：2025-02-04

    申请号：US17698805

    申请日：2022-03-18

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

    Inventor： Zongwei Zhou ,  Jae Shin ,  Jianming Liang

    IPC: G06V10/82 ,  G06F18/21 ,  G06F18/214 ,  G06T7/00 ,  G06V10/764 ,  G16H30/40

    Abstract: Described herein are systems, methods, and apparatuses for actively and continually fine-tuning convolutional neural networks to reduce annotation requirements, in which the trained networks are then utilized in the context of medical imaging. The success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places. However, it is tedious, laborious, and time consuming to create large annotated datasets, and demands costly, specialty-oriented skills. A novel method to naturally integrate active learning and transfer learning (fine-tuning) into a single framework is presented to dramatically reduce annotation cost, starting with a pre-trained CNN to seek “worthy” samples for annotation and gradually enhances the (fine-tuned) CNN via continual fine-tuning. The described method was evaluated using three distinct medical imaging applications, demonstrating that it can reduce annotation efforts by at least half compared with random selection.

    SYSTEMS, METHODS, AND APPARATUSES FOR SYSTEMATICALLY DETERMINING AN OPTIMAL APPROACH FOR THE COMPUTER-AIDED DIAGNOSIS OF A PULMONARY EMBOLISM
    15.
    发明申请
    SYSTEMS, METHODS, AND APPARATUSES FOR SYSTEMATICALLY DETERMINING AN OPTIMAL APPROACH FOR THE COMPUTER-AIDED DIAGNOSIS OF A PULMONARY EMBOLISM 有权

    公开(公告)号：US20230081305A1

    公开(公告)日：2023-03-16

    申请号：US17944881

    申请日：2022-09-14

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

    Inventor： Nahid Ul Islam ,  Shiv Gehlot ,  Zongwei Zhou ,  Jianming Liang

    IPC: G06T7/00 ,  G06T3/40 ,  G06V10/774 ,  G06V10/77 ,  G06V10/82 ,  A61B6/00 ,  G16H50/20

    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.

    Methods, systems, and media for discriminating and generating translated images
    17.
    发明授权
    Methods, systems, and media for discriminating and generating translated images 有权

    公开(公告)号：US11164021B2

    公开(公告)日：2021-11-02

    申请号：US16875680

    申请日：2020-05-15

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

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

    IPC: G06K9/62 ,  G06K9/00

    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.

    Methods, systems, and media for selecting candidates for annotation for use in training classifiers
    18.
    发明授权
    Methods, systems, and media for selecting candidates for annotation for use in training classifiers 有权

    公开(公告)号：US10956785B2

    公开(公告)日：2021-03-23

    申请号：US16397990

    申请日：2019-04-29

    Applicant: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY

    Inventor： Jianming Liang ,  Zongwei Zhou ,  Jae Shin

    IPC: G06K9/00 ,  G06K9/62

    Abstract: Methods, systems, and media for selecting candidates for annotation for use in training classifiers are provided. In some embodiments, the method comprises: identifying, for a trained Convolutional Neural Network (CNN), a group of candidate training samples, wherein each candidate training sample includes a plurality of patches; for each patch of the plurality of patches, determining a plurality of probabilities, each probability being a probability that the patch corresponds to a label of a plurality of labels; identifying a subset of the patches in the plurality of patches; for each patch in the subset of the patches, calculating a metric that indicates a variance of the probabilities assigned to each patch; selecting a subset of the candidate training samples based on the metric; labeling candidate training samples in the subset of the candidate training samples by querying an external source; and re-training the CNN using the labeled candidate training samples.

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