Patents by Inventor Teresa Wu
Teresa Wu has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11861475Abstract: Described here are systems and methods for generating and implementing a hybrid machine learning and mechanistic model to produce biological feature maps, or other measurements of biological features, based on an input of multiparametric magnetic resonance or other images. The hybrid model can include a combination of a machine learning model and a mechanistic model that takes as an input multiparametric MRI, or other imaging, data to generate biological feature maps (e.g., tumor cell density maps), or other measures or predictions of biological features (e.g., tumor cell density). The hybrid models have capabilities of learning individual-specific relationships between imaging features and biological features.Type: GrantFiled: November 19, 2018Date of Patent: January 2, 2024Assignees: Mayo Foundation for Medical Education and Research, Arizona Board of Regents on behalf of Arizona State UniversityInventors: Leland S. Hu, Jing Li, Kristin R. Swanson, Teresa Wu, Nathan Gaw, Hyunsoo Yoon, Andrea Hawkins-Daarud
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Patent number: 11651862Abstract: A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing deep learning. More specifically, the system and method produce predictions of MCI conversions to Alzheimer's/dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is a deep learned model trained using transfer learning. An MCI-DAP server may then receive a request from a clinician to process predictions related to a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.Type: GrantFiled: July 15, 2022Date of Patent: May 16, 2023Assignee: MS TECHNOLOGIESInventors: Yuan-Ming Fleming Lure, Jing Li, Teresa Wu, David Weidman, Kewei Chen, Xiaonan Liu, Yi Su
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Publication number: 20230042243Abstract: A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing hybrid machine learning. More specifically, the system and method produce predictions of MCI conversions to dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is trained using transfer learning. A platform may then receive a request from a clinician for a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.Type: ApplicationFiled: October 20, 2022Publication date: February 9, 2023Inventors: Yuan-Ming Fleming Lure, Jing Li, Teresa Wu, David Weidman, Kewei Chen, Xiaonan Liu, Yi Su
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Publication number: 20220367056Abstract: A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing deep learning. More specifically, the system and method produce predictions of MCI conversions to Alzheimer's/dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is a deep learned model trained using transfer learning. An MCI-DAP server may then receive a request from a clinician to process predictions related to a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.Type: ApplicationFiled: July 15, 2022Publication date: November 17, 2022Inventors: Yuan-Ming Fleming Lure, Jing Li, Teresa Wu, David Weidman, Kewei Chen, Xiaonan Liu, Yi Su
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Publication number: 20220344051Abstract: A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing deep learning. More specifically, the system and method produce predictions of MCI conversions to Alzheimer's/dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is a deep learned model trained using transfer learning. An MCI-DAP server may then receive a request from a clinician to process predictions related to a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.Type: ApplicationFiled: July 5, 2022Publication date: October 27, 2022Inventors: Yuan-Ming Fleming Lure, Jing Li, Teresa Wu, David Weidman, Kewei Chen, Xiaonan Liu, Yi Su
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Publication number: 20220318999Abstract: A system for blob detection using deep learning is disclosed. The system may include a non-transitory computer-readable storage medium configured to store a plurality of instructions thereon which, when executed by a processor, cause the system to train a U-Net and generate a probability map including a plurality of centroids of a plurality of corresponding blobs, derive two distance maps with bounded probabilities, apply Difference of Gaussian (DoG) with an adaptive scale constrained by the two distance maps with the bounded probabilities, and apply Hessian analysis and perform a blob segmentation.Type: ApplicationFiled: March 18, 2022Publication date: October 6, 2022Inventors: YANZHE XU, TERESA WU, FEI GAO
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Publication number: 20220301172Abstract: Methods that implement image-guided tissue analysis, MRI-based computational modeling, and imaging informatics to analyze the diversity and dynamics of molecularly-distinct subpopulations and the evolving competitive landscapes in human glioblastoma multiforme (“GBM”) are provided. Machine learning models are constructed based on multiparametric MRI data and molecular data (e.g., CNV, exome, gene expression). Models can also be built based on specific biological factors, such as sex and age. Inputting MRI data into the trained predictive models generates maps that depict spatial patterns of molecular markers, which can be used to quantify and co-localize regions molecularly distinct subpopulations in tumors and other regions, such as the non-enhancing parenchyma, or brain around tumor (“BAT”) regions.Type: ApplicationFiled: May 20, 2022Publication date: September 22, 2022Inventors: Leland S. Hu, Kristin R. Swanson, J. Ross Mitchell, Nhan L. Tran, Jing Li, Teresa Wu
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Publication number: 20220262514Abstract: A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis. More specifically, the system and method produce predictions of MCI conversions to dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is trained using transfer learning. A server may then receive a request from a clinician for a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.Type: ApplicationFiled: December 22, 2021Publication date: August 18, 2022Inventors: Yuan-Ming Fleming Lure, Jing Li, Teresa Wu, David Weidman, Kewei Chen, Xiaonan Liu
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Patent number: 11341649Abstract: Methods that implement image-guided tissue analysis, MRI-based computational modeling, and imaging informatics to analyze the diversity and dynamics of molecularly-distinct subpopulations and the evolving competitive landscapes in human glioblastoma multiforme (“GBM”) are provided. Machine learning models are constructed based on multiparametric MRI data and molecular data (e.g., CNV, exome, gene expression). Models can also be built based on specific biological factors, such as sex and age. Inputting MRI data into the trained predictive models generates maps that depict spatial patterns of molecular markers, which can be used to quantify and co-localize regions molecularly distinct subpopulations in tumors and other regions, such as the non-enhancing parenchyma, or brain around tumor (“BAT”) regions.Type: GrantFiled: February 26, 2019Date of Patent: May 24, 2022Assignees: Mayo Foundation for Medical Education and Research, Arizona Board of RegentsInventors: Leland S. Hu, Kristin R. Swanson, J. Ross Mitchell, Nhan L. Tran, Jing Li, Teresa Wu
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Publication number: 20220148731Abstract: Genetic and/or other biological marker prediction data are generated based on inputting medical image data to a suitably trained machine learning model, where the output genetic prediction data not only indicate a prediction of genetic features and/or other biological markers for a subject, but also a measure of uncertainty in each of those predictions. As an example, a transductive learning Gaussian process model is used to generate the genetic and/or other biological marker predication data and corresponding predictive uncertainty data. As another example, a knowledge-infused global-local data fusion model can be used for spatial predictive modeling.Type: ApplicationFiled: November 11, 2021Publication date: May 12, 2022Inventors: Andrea J. Hawkins-Daarud, Leland S. Hu, Kristin R. Swanson, Teresa Wu, Jing Li, Lujia Wang
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Patent number: 10909675Abstract: A system and method for characterizing tissues of a subject using multi-parametric imaging are provided. In some aspects, the method includes receiving a set of multi-parametric magnetic resonance (“MR”) images acquired from a subject using an MR imaging system, and selecting at least one region of interest (“ROI”) in the subject using one or more images in the set of multi-parametric MR images. The method also includes performing a texture analysis on corresponding ROIs in the set of multi-parametric MR images to generate a set of texture features, and applying a classification scheme, using the set of texture features, to characterize tissues in the ROI. The method further includes generating a report indicative of characterized tissues in the ROI.Type: GrantFiled: October 11, 2016Date of Patent: February 2, 2021Assignees: Mayo Foundation for Medical Education and Research, Arizona Board of Regents on Behalf of Arizona State UniversityInventors: Leland S. Hu, J. Ross Mitchell, Jing Li, Teresa Wu
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Publication number: 20200410683Abstract: Methods that implement image-guided tissue analysis, MRI-based computational modeling, and imaging informatics to analyze the diversity and dynamics of molecularly-distinct subpopulations and the evolving competitive landscapes in human glioblastoma multiforme (“GBM”) are provided. Machine learning models are constructed based on multiparametric MRI data and molecular data (e.g., CNV, exome, gene expression). Models can also be built based on specific biological factors, such as sex and age. Inputting MRI data into the trained predictive models generates maps that depict spatial patterns of molecular markers, which can be used to quantify and co-localize regions molecularly distinct subpopulations in tumors and other regions, such as the non-enhancing parenchyma, or brain around tumor (“BAT”) regions.Type: ApplicationFiled: February 26, 2019Publication date: December 31, 2020Inventors: Leland S. Hu, Kristin R. Swanson, J. Ross Mitchell, Nhan L. Tran, Jing Li, Teresa Wu
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Publication number: 20200342359Abstract: Described here are systems and methods for generating and implementing a hybrid machine learning and mechanistic model to produce biological feature maps, or other measurements of biological features, based on an input of multiparametric magnetic resonance or other images. The hybrid model can include a combination of a machine learning model and a mechanistic model that takes as an input multiparametric MRI, or other imaging, data to generate biological feature maps (e.g., tumor cell density maps), or other measures or predictions of biological features (e.g., tumor cell density). The hybrid models have capabilities of learning individual-specific relationships between imaging features and biological features.Type: ApplicationFiled: November 19, 2018Publication date: October 29, 2020Inventors: Leland S. Hu, Jing Li, Kristin R. Swanson, Teresa Wu, Nathan Gaw, Hyunsoo Yoon, Andrea Hawkins-Daarud
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Patent number: 10235582Abstract: The present disclosure describes systems and methods for assessing biometric data and determining the type of additional processing required to conclude analysis. In one example, the disclosure describes a computer-implemented method comprising providing biometric data, defining one or more performance parameters, assessing the biometric data for quality of one or more features, wherein the quality includes at least a quantity and correlation between the one or more features, assessing the rarity of the one or more features, and processing the performance parameter, quality, and rarity to guide a determination of a type of additional processing.Type: GrantFiled: July 31, 2015Date of Patent: March 19, 2019Assignee: GEMALTO SAInventors: Cedric Neumann, Teresa Wu
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Patent number: 10045728Abstract: Methods and systems for identifying blobs, for example kidney glomeruli, are disclosed. A raw image may be smoothed via a difference of Gaussians filter, and a Hessian analysis may be conducted on the smoothed image to mark glomeruli candidates. Exemplary candidate features are identified, such as average intensity AT, likelihood of blobness RT, and flatness ST. A clustering algorithm may be utilized to post prune the glomeruli candidates.Type: GrantFiled: March 28, 2016Date of Patent: August 14, 2018Assignee: Arizona Board of Regents on behalf of Arizona State UniversityInventors: Teresa Wu, Min Zhang
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Publication number: 20170236017Abstract: The present disclosure describes systems and methods for assessing biometric data and determining the type of additional processing required to conclude analysis. In one example, the disclosure describes a computer-implemented method comprising providing biometric data, defining one or more performance parameters, assessing the biometric data for quality of one or more features, wherein the quality includes at least a quantity and correlation between the one or more features, assessing the rarity of the one or more features, and processing the performance parameter, quality, and rarity to guide a determination of a type of additional processing.Type: ApplicationFiled: July 31, 2015Publication date: August 17, 2017Inventors: Cedric NEUMANN, Teresa WU
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Publication number: 20160206235Abstract: Methods and systems for identifying blobs, for example kidney glomeruli, are disclosed. A raw image may be smoothed via a difference of Gaussians filter, and a Hessian analysis may be conducted on the smoothed image to mark glomeruli candidates. Exemplary candidate features are identified, such as average intensity AT, likelihood of blobness RT, and flatness ST. A clustering algorithm may be utilized to post prune the glomeruli candidates.Type: ApplicationFiled: March 28, 2016Publication date: July 21, 2016Inventors: Teresa Wu, Min Zhang
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Publication number: 20160190805Abstract: The apparatus, systems and methods herein facilitate generation of energy-related revenue for an energy customer of an electricity supplier. The apparatuses and methods herein can be used to generate suggested operating schedules for the energy assets that including a controllable energy asset, using an objective function. The objective function is determined based on a dynamic simulation model of the energy profile of the energy assets. The dynamic simulation model is adaptive to physical changes in the energy assets based on a parametric estimation using at least one model parameter. The model parameter is at least one of an operation characteristic of the controllable energy asset, a thermodynamic property of the energy assets, and a projected environmental condition. Energy-related revenue available to the energy customer is based at least in part on a wholesale electricity market or on a regulation market.Type: ApplicationFiled: July 21, 2015Publication date: June 30, 2016Applicant: Viridity Energy, Inc.Inventors: Alain P. Steven, Eunice B. Hameyie, Jin Wen, Teresa Wu, Ajay Sunder
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Publication number: 20160180474Abstract: The disclosure facilitates generation of energy-related revenue for an energy customer of an electricity supplier. The disclosure herein can be implemented to generate suggested operating schedules for energy assets that include a controllable energy asset, using an objective function. The objective function is determined based on a dynamic simulation model of the energy profile of the energy assets. The dynamic simulation model is adaptive to physical changes in the energy assets based at least in part on a physical model of the thermodynamic property of the at least one energy asset and at least in part on data representative of an operation characteristic of the controllable energy asset, a thermodynamic property of the energy assets, and/or a projected environmental condition. Energy-related revenue available to the energy customer is based at least in part on a wholesale electricity market or on a regulation market.Type: ApplicationFiled: July 21, 2015Publication date: June 23, 2016Applicant: VIRIDITY ENERGY, INC.Inventors: Alain P. Steven, Eunice B. Hameyie, Jin Wen, Teresa Wu
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Patent number: 9171276Abstract: The disclosure facilitates generation of energy-related revenue for an energy customer of an electricity supplier. The disclosure herein can be implemented to generate suggested operating schedules for energy assets that include a controllable energy asset, using an objective function. The objective function is determined based on a dynamic simulation model of the energy profile of the energy assets. The dynamic simulation model is adaptive to physical changes in the energy assets based at least in part on a physical model of the thermodynamic property of the at least one energy asset and at least in part on data representative of an operation characteristic of the controllable energy asset, a thermodynamic property of the energy assets, and/or a projected environmental condition. Energy-related revenue available to the energy customer is based at least in part on a wholesale electricity market or on a regulation market.Type: GrantFiled: May 6, 2013Date of Patent: October 27, 2015Assignee: VIRIDITY ENERGY, INC.Inventors: Alain P. Steven, Eunice B. Hameyie, Jin Wen, Teresa Wu