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公开(公告)号：US20230409673A1
公开(公告)日：2023-12-21
申请号：US17807761
申请日：2022-06-20
发明人： Ravishankar Hariharan , Rohan Keshav Patil , Rahul Venkataramani , Prasad Sudhakara Murthy , Deepa Anand , Utkarsh Agrawal
CPC分类号： G06K9/6265 , G06K9/6227 , G06N3/02
摘要： Systems/techniques that facilitate improved uncertainty scoring for neural networks via stochastic weight perturbations are provided. In various embodiments, a system can access a trained neural network and/or a data candidate on which the trained neural network is to be executed. In various aspects, the system can generate an uncertainty indicator representing how confidently executable or how unconfidently executable the trained neural network is with respect to the data candidate, based on a set of perturbed instantiations of the trained neural network.
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公开(公告)号：US20230177747A1
公开(公告)日：2023-06-08
申请号：US17543234
申请日：2021-12-06
发明人： Rajesh Veera Venkata Lakshmi Langoju , Utkarsh Agrawal , Bipul Das , Risa Shigemasa , Yasuhiro Imai , Jiang Hsieh
CPC分类号： G06T11/008 , G06N20/20 , G06T5/002 , G06T5/50
摘要： Systems/techniques that facilitate machine learning generation of low-noise and high structural conspicuity images are provided. In various embodiments, a system can access an image and can apply at least one of image denoising or image resolution enhancement to the image, thereby yielding a first intermediary image. In various instances, the system can generate, via execution of a plurality of machine learning models, a plurality of second intermediary images based on the first intermediary image, wherein a given machine learning model in the plurality of machine learning models receives as input the first intermediary image, wherein the given machine learning model produces as output a given second intermediary image in the plurality of second intermediary images, and wherein the given second intermediary image represents a kernel-transformed version of the first intermediary image. In various cases, the system can generate a blended image based on the plurality of second intermediary images.
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公开(公告)号：US20230029188A1
公开(公告)日：2023-01-26
申请号：US17385600
申请日：2021-07-26
发明人： Rajesh Langoju , Utkarsh Agrawal , Bhushan Patil , Vanika Singhal , Bipul Das , Jiang Hsieh
摘要： The current disclosure provides methods and systems to reduce an amount of structured and unstructured noise in image data. Specifically, a multi-stage deep learning method is provided, comprising training a deep learning network using a set of training pairs interchangeably including input data from a first noisy dataset with a first noise level and target data from a second noisy dataset with a second noise level, and input data from the second noisy dataset and target data from the first noisy dataset; generating an ultra-low noise data equivalent based on a low noise data fed into the trained deep learning network; and retraining the deep learning network on the set of training pairs using the target data of the set of training pairs in a first retraining step, and using the ultra-low noise data equivalent as target data in a second retraining step.
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公开(公告)号：US20230018833A1
公开(公告)日：2023-01-19
申请号：US17379003
申请日：2021-07-19
发明人： Bipul Das , Rakesh Mullick , Utkarsh Agrawal , KS Shriram , Sohan Ranjan , Tao Tan
摘要： Techniques are described for generating multimodal training data cohorts tailored to specific clinical machine learning (ML) model inferencing tasks. In an embodiment, a method comprises accessing, by a system comprising a processor, multimodal clinical data for a plurality of subjects included in one or more clinical data sources. The method further comprises selecting, by the system, datasets from the multimodal clinical data based on the datasets respectively comprising subsets of the multimodal clinical data that satisfy criteria determined to be relevant to a clinical processing task. The method further comprises generating, by the system, a training data cohort comprising the datasets for training a clinical inferencing model to perform the clinical processing task.
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公开(公告)号：US20240160915A1
公开(公告)日：2024-05-16
申请号：US18055648
申请日：2022-11-15
IPC分类号： G06N3/08
CPC分类号： G06N3/08
摘要： Systems/techniques that facilitate explainable deep interpolation are provided. In various embodiments, a system can access a data candidate, wherein a set of numerical elements of the data candidate are missing. In various aspects, the system can generate, via execution of a deep learning neural network on the data candidate, a set of weight maps for the set of missing numerical elements. In various instances, the system can compute the set of missing numerical elements by respectively combining, according to the set of weight maps, available interpolation neighbors of the set of missing numerical elements.
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公开(公告)号：US11727086B2
公开(公告)日：2023-08-15
申请号：US17093960
申请日：2020-11-10
发明人： Tao Tan , Gopal B. Avinash , Máté Fejes , Ravi Soni , Dániel Attila Szabó , Rakesh Mullick , Vikram Melapudi , Krishna Seetharam Shriram , Sohan Rashmi Ranjan , Bipul Das , Utkarsh Agrawal , László Ruskó , Zita Herczeg , Barbara Darázs
IPC分类号： G06F18/214 , G06T7/30 , G06N5/04 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/50 , A61B6/03 , A61B6/00 , A61B5/055 , A61B5/00 , G06T5/50 , G06F18/22 , G06F18/28 , G06F18/21
CPC分类号： G06F18/214 , A61B5/055 , A61B5/7267 , A61B6/032 , A61B6/5223 , G06F18/2178 , G06F18/22 , G06F18/28 , G06N5/04 , G06T5/50 , G06T7/30 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/50 , G06T2200/04 , G06T2207/10081 , G06T2207/10116 , G06T2207/20081 , G06T2207/20084 , G06T2207/20212 , G06T2207/30004 , G06V2201/03
摘要： Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.
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公开(公告)号：US20230048231A1
公开(公告)日：2023-02-16
申请号：US17444881
申请日：2021-08-11
发明人： Rajesh Langoju , Utkarsh Agrawal , Risa Shigemasa , Bipul Das , Yasuhiro Imai , Jiang Hsieh
摘要： Various methods and systems are provided for computed tomography imaging. In one embodiment, a method includes acquiring, with an x-ray detector and an x-ray source coupled to a gantry, a three-dimensional image volume of a subject while the subject moves through a bore of the gantry and the gantry rotates the x-ray detector and the x-ray source around the subject, inputting the three-dimensional image volume to a trained deep neural network to generate a corrected three-dimensional image volume with a reduction in aliasing artifacts present in the three-dimensional image volume, and outputting the corrected three-dimensional image volume. In this way, aliasing artifacts caused by sub-sampling may be removed from computed tomography images while preserving details, texture, and sharpness in the computed tomography images.
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公开(公告)号：US20210406681A1
公开(公告)日：2021-12-30
申请号：US16987449
申请日：2020-08-07
摘要： Techniques are provided for learning loss functions using DL networks and integrating these loss functions into DL based image transformation architectures. In one embodiment, a method is provided that comprising facilitating training, by a system operatively coupled to a processor, a first deep learning network to predict a loss function metric value of a loss function. The method further comprises employing, by the system, the first deep learning network to predict the loss function metric value in association with training a second deep learning network that to perform a defined deep learning task. In various embodiments, the loss function comprises a computationally complex loss function that is not easily implementable in existing deep learning packages, such as a non-differentiable loss function, a feature similarity index match (FSIM) loss function, a system transfer function, a visual information fidelity (VIF) loss function and the like.
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公开(公告)号：US12131446B2
公开(公告)日：2024-10-29
申请号：US17368534
申请日：2021-07-06
发明人： Rajesh Veera Venkata Lakshmi Langoju , Prasad Sudhakara Murthy , Utkarsh Agrawal , Bhushan D. Patil , Bipul Das
IPC分类号： G06T5/73 , G06N20/00 , G06T3/4053 , G06T5/20
CPC分类号： G06T5/73 , G06N20/00 , G06T3/4053 , G06T5/20 , G06T2207/20081
摘要： Systems/techniques that facilitate self-supervised deblurring are provided. In various embodiments, a system can access an input image generated by an imaging device. In various aspects, the system can train, in a self-supervised manner based on a point spread function of the imaging device, a machine learning model to deblur the input image. More specifically, the system can append to the model one or more non-trainable convolution layers having a blur kernel that is based on the point spread function of the imaging device. In various aspects, the system can feed the input image to the model, the model can generate a first output image based on the input image, the one or more non-trainable convolution layers can generate a second output image by convolving the first output image with the blur kernel, and the system can update parameters of the model based on a difference between the input image and the second output image.
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公开(公告)号：US20240265591A1
公开(公告)日：2024-08-08
申请号：US18164372
申请日：2023-02-03
发明人： Bipul Das , Utkarsh Agrawal , Prasad Sudhakara Murthy , Risa Shigemasa , Kentaro Ogata , Yasuhiro Imai
CPC分类号： G06T11/005 , G06T3/4053 , G06V10/44 , G06V10/806 , G06T2207/10081 , G06T2207/10116 , G06T2211/408
摘要： Methods and systems are provided for interpolating missing views in dual-energy computed tomography data. In one example, a method includes obtaining a first sinogram missing a plurality of views and a second sinogram missing a different plurality of views, the first sinogram acquired with a first X-ray source energy during a scan and the second sinogram acquired with a second, different X-ray source energy during the scan; initializing each of the first sinogram and the second sinogram to form a first initialized sinogram and a second initialized sinogram; entering the first initialized sinogram and the second initialized sinogram into the same or different interpolation models trained to output a first filled sinogram based on the first initialized sinogram and output a second filled sinogram based on the second initialized sinogram; and reconstructing one or more images from the first filled sinogram and the second filled sinogram.
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