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    SYSTEMS, METHODS, AND APPARATUSES FOR IMPLEMENTING ADVANCEMENTS TOWARDS ANNOTATION EFFICIENT DEEP LEARNING IN COMPUTER-AIDED DIAGNOSIS
    3.
    发明申请
    SYSTEMS, METHODS, AND APPARATUSES FOR IMPLEMENTING ADVANCEMENTS TOWARDS ANNOTATION EFFICIENT DEEP LEARNING IN COMPUTER-AIDED DIAGNOSIS 有权

    公开(公告)号：US20220328189A1

    公开(公告)日：2022-10-13

    申请号：US17716929

    申请日：2022-04-08

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

    Inventor： Zongwei Zhou ,  Jianming Liang

    IPC: G16H50/20 ,  G06V10/774

    Abstract: Embodiments described herein include systems for implementing annotation-efficient deep learning in computer-aided diagnosis. Exemplary embodiments include systems having a processor and a memory specially configured with instructions for learning annotation-efficient deep learning from non-labeled medical images to generate a trained deep-learning model by applying a multi-phase model training process via specially configured instructions for pre-training a model by executing a one-time learning procedure using an initial annotated image dataset; iteratively re-training the model by executing a fine-tuning learning procedure using newly available annotated images without re-using any images from the initial annotated image dataset; selecting a plurality of most representative samples related to images of the initial annotated image dataset and the newly available annotated images by executing an active selection procedure based on the which of a collection of un-annotated images exhibit either a greatest uncertainty or a greatest entropy; extracting generic image features; updating the model using the generic image features extrated; and outputting the model as the trained deep-learning model for use in analyzing a patient medical image. Other related embodiments are disclosed.

    Systems, methods, and apparatuses for systematically determining an optimal approach for the computer-aided diagnosis of a pulmonary embolism
    6.
    发明授权
    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：US12236592B2

    公开(公告)日：2025-02-25

    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 ,  A61B6/50 ,  G06T3/40 ,  G06V10/77 ,  G06V10/774 ,  G06V10/82 ,  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.

    SYSTEMS, METHODS, AND APPARATUSES FOR ACTIVELY AND CONTINUALLY FINE-TUNING CONVOLUTIONAL NEURAL NETWORKS TO REDUCE ANNOTATION REQUIREMENTS
    8.
    发明申请
    SYSTEMS, METHODS, AND APPARATUSES FOR ACTIVELY AND CONTINUALLY FINE-TUNING CONVOLUTIONAL NEURAL NETWORKS TO REDUCE ANNOTATION REQUIREMENTS 有权

    公开(公告)号：US20220300769A1

    公开(公告)日：2022-09-22

    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: G06K9/62 ,  G16H30/40 ,  G06V10/82 ,  G06V10/764 ,  G06T7/00

    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 THE USE OF TRANSFERABLE VISUAL WORDS FOR AI MODELS THROUGH SELF-SUPERVISED LEARNING IN THE ABSENCE OF MANUAL LABELING FOR THE PROCESSING OF MEDICAL IMAGING
    10.
    发明申请
    SYSTEMS, METHODS, AND APPARATUSES FOR THE USE OF TRANSFERABLE VISUAL WORDS FOR AI MODELS THROUGH SELF-SUPERVISED LEARNING IN THE ABSENCE OF MANUAL LABELING FOR THE PROCESSING OF MEDICAL IMAGING 有权

    公开(公告)号：US20210343014A1

    公开(公告)日：2021-11-04

    申请号：US17246032

    申请日：2021-04-30

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

    Inventor： Fatemeh Haghighi ,  Mohammad Reza Hosseinzadeh Taher ,  Zongwei Zhou ,  Jianming Liang

    IPC: G06T7/00 ,  G06T3/00 ,  G06K9/32 ,  G06K9/46 ,  G06K9/62

    Abstract: Described herein are means for the generation of semantic genesis models through self-supervised learning in the absence of manual labeling, in which the trained semantic genesis models are then utilized for the processing of medical imaging. For instance, an exemplary system is specially configured with means for performing a self-discovery operation which crops 2D patches or crops 3D cubes from similar patient scans received at the system as input; means for transforming each anatomical pattern represented within the cropped 2D patches or the cropped 3D cubes to generate transformed 2D anatomical patterns or transformed 3D anatomical patterns; means for performing a self-classification operation of the transformed anatomical patterns by formulating a C-way multi-class classification task for representation learning; means for performing a self-restoration operation by recovering original anatomical patterns from the transformed 2D patches or transformed 3D cubes having transformed anatomical patterns embedded therein to learn different sets of visual representation; and means for providing a semantics-enriched pre-trained AI model having a trained encoder-decoder structure with skip connections in between based on the performance of the self-discovery operation, the self-classification operation, and the self-restoration operation. Other related embodiments are disclosed.

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