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公开(公告)号：US20240339200A1
公开(公告)日：2024-10-10
申请号：US18627831
申请日：2024-04-05
Inventor： DongAo Ma , Jiaxuan Pang , Jianming Liang
IPC: G16H30/40 , G06V10/764 , G16H30/20
CPC classification number: G16H30/40 , G06V10/764 , G16H30/20
Abstract: Exemplary systems include means for receiving medical image data at the system from a plurality of datasets provided via publicly available sources; evaluating the medical image data for the presence of expert notation embedded within the medical image data; determining the expert notations embedded within the medical image data are formatted using inconsistent and heterogeneous labeling across the plurality of datasets; generating an interim AI model by applying a task head classifier to learn the annotations of the expert notations embedded within the medical image data to generate an interim AI model; scaling the interim AI model having the learned annotations of the expert notations embedded therein to additional tasks by applying multi-task heads using cyclical pre-training of the interim AI model trained previously to generate task-specific AI models, with each respective task-specific AI model having differently configured task-specific learning objectives; training a pre-trained AI model specially configured for an application-specific target task by applying task re-visitation training forcing the pre-trained AI model being trained to re-visit all tasks in each round of training and forcing the pre-trained AI model being trained to re-use all accrued knowledge to improve learning by the pre-trained AI model being trained against the current application-specific target task for which the pre-trained AI model is being trained.
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公开(公告)号：US20230306723A1
公开(公告)日：2023-09-28
申请号：US18126318
申请日：2023-03-24
Inventor： DongAo Ma , Jiaxuan Pang , Nahid Ul Islam , Mohammad Reza Hosseinzadeh Taher , Fatemeh Haghighi , Jianming Liang
IPC: G06T7/00 , G06V10/776 , G06V10/774 , G06V10/764 , G06N3/0895
CPC classification number: G06V10/774 , G06N3/0895 , G06T7/0012 , G06V10/764 , G06V10/776 , G06T2207/10116 , G06T2207/20081 , G06T2207/20092 , G06T2207/30004 , G06V2201/03
Abstract: Described herein are systems, methods, and apparatuses for implementing self-supervised domain-adaptive pre-training via a transformer for use with medical image classification in the context of medical image analysis. An exemplary system includes means for receiving a first set of training data having non-medical photographic images; receiving a second set of training data with medical images; pre-training an AI model on the first set of training data with the non-medical photographic images; performing domain-adaptive pre-training of the AI model via self-supervised learning operations using the second set of training data having the medical images; generating a trained domain-adapted AI model by fine-tuning the AI model against the targeted medical diagnosis task using the second set of training data having the medical images; outputting the trained domain-adapted AI model; and executing the trained domain-adapted AI model to generate a predicted medical diagnosis from an input image not present within the training data.
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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：US20230306562A1
公开(公告)日：2023-09-28
申请号：US18126317
申请日：2023-03-24
Inventor： Jiaxuan Pang , DongAo Ma , Jiangming Liang
CPC classification number: G06T5/005 , G06T7/0012 , G06T5/10 , G16H30/40 , G16H50/20 , G06T2207/30004 , G06T2207/20081 , G06T2207/20048 , G06T2207/20032 , G06T2207/20192 , G06T2207/20084 , G06T2207/20021 , G06T2207/20092
Abstract: Described herein are means for performing self-supervised visual representation learning using order and appearance recovery on a vision transformer. An exemplary system having a processor and memory is specially configured to execute instructions including: receiving medical image training data; selecting a medical image; generating a first perturbed image by applying local pixel shuffling and other image perturbations and outputting a first patchified perturbed image; generating a second randomized patchified image by patchifying and applying a random permutation to the original image; inputting the first patchified perturbed image and the second randomized patchified image into first and second transformer encoders which each generate and then share first and second generated weights through the recovery of both and patch order appearance from each image; and outputting a pre-trained AI model to perform medical image diagnosis on a new medical image absent from the training data input received by the system.
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