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    SYSTEMS, METHODS, AND APPARATUSES FOR GENERATING PRE-TRAINED MODELS FOR nnU-Net THROUGH THE USE OF IMPROVED TRANSFER LEARNING TECHNIQUES
    31.
    发明申请
    SYSTEMS, METHODS, AND APPARATUSES FOR GENERATING PRE-TRAINED MODELS FOR nnU-Net THROUGH THE USE OF IMPROVED TRANSFER LEARNING TECHNIQUES 有权

    公开(公告)号：US20230072400A1

    公开(公告)日：2023-03-09

    申请号：US17939783

    申请日：2022-09-07

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

    Inventor： Shivam Bajpai ,  Jianming Liang

    IPC: G06T7/00 ,  G06V10/77 ,  G06V10/46 ,  G06V10/70

    Abstract: Described herein are means for generating pre-trained models for nnU-Net through the use of improved transfer learning techniques, in which the pre-trained models are then utilized for the processing of medical imaging. According to a particular embodiment, there is a system specially configured for segmenting medical images, in which such a system includes: a memory to store instructions; a processor to execute the instructions stored in the memory; wherein the system is specially configured to: execute instructions via the processor for executing a pre-trained model from Models Genesis within a nnU-Net framework; execute instructions via the processor for learning generic anatomical patterns within the executing Models Genesis through self-supervised learning; execute instructions via the processor for transforming an original image using distortion and cutout-based methods; execute instructions via the processor for learning the reconstruction of the original image from the transformed image using an encoder-decoder architecture of the nnU-Net framework to identify the generic anatomical representation from the transformed image by recovering the original image; and wherein architecture determined by the nnU-Net framework is utilized with Models Genesis and is trained to minimize the L2 distance between the prediction and ground truth. Other related embodiments are disclosed.

    SYSTEMS, METHODS, AND APPARATUSES FOR IMPLEMENTING ANNOTATION-EFFICIENT DEEP LEARNING MODELS UTILIZING SPARSELY-ANNOTATED OR ANNOTATION-FREE TRAINING
    32.
    发明申请
    SYSTEMS, METHODS, AND APPARATUSES FOR IMPLEMENTING ANNOTATION-EFFICIENT DEEP LEARNING MODELS UTILIZING SPARSELY-ANNOTATED OR ANNOTATION-FREE TRAINING 有权

    公开(公告)号：US20220405933A1

    公开(公告)日：2022-12-22

    申请号：US17843817

    申请日：2022-06-17

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

    Inventor： Nima Tajbakhsh ,  Jianming Liang

    IPC: G06T7/00 ,  G06V10/82 ,  G06V10/774 ,  G16H50/20

    Abstract: Described herein are means for implementing annotation-efficient deep learning models utilizing sparsely-annotated or annotation-free training, in which trained models are then utilized for the processing of medical imaging. An exemplary system includes at least a processor and a memory to execute instructions for learning anatomical embeddings by forcing embeddings learned from multiple modalities; initiating a training sequence of an AI model by learning dense anatomical embeddings from unlabeled date, then deriving application-specific models to diagnose diseases with a small number of examples; executing collaborative learning to generate pretrained multimodal models; training the AI model using zero-shot or few-shot learning; embedding physiological and anatomical knowledge; embedding known physical principles refining the AI model; and outputting a trained AI model for use in diagnosing diseases and abnormal conditions in medical imaging. Other related embodiments are disclosed.

    SYSTEMS, METHODS, AND APPARATUSES FOR IMPLEMENTING SYSTEMATIC BENCHMARKING ANALYSIS TO IMPROVE TRANSFER LEARNING FOR MEDICAL IMAGE ANALYSIS
    39.
    发明申请
    SYSTEMS, METHODS, AND APPARATUSES FOR IMPLEMENTING SYSTEMATIC BENCHMARKING ANALYSIS TO IMPROVE TRANSFER LEARNING FOR MEDICAL IMAGE ANALYSIS 有权

    公开(公告)号：US20230116897A1

    公开(公告)日：2023-04-13

    申请号：US17961896

    申请日：2022-10-07

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

    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.

    SYSTEMS, METHODS, AND APPARATUSES FOR IMPLEMENTING ADVANCEMENTS TOWARDS ANNOTATION EFFICIENT DEEP LEARNING IN COMPUTER-AIDED DIAGNOSIS
    40.
    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.

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