Patents by Inventor Priya Krishnan
Priya Krishnan 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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Publication number: 20250191124Abstract: A method may include training a machine learning model to reconstruct, based at least on a first image having a first spatial resolution, a second image having a second spatial resolution lower than the first spatial resolution. The reconstruction may include an iterative up-projection and down-projection of the second image to generate a third image having a third spatial resolution higher than the second spatial resolution. The training may include adjusting the machine learning model to minimize a first error between a target image having a target resolution and the third image and a second error between the second image and a fourth image generated by down-projection of a first up-projection of the second image. The method may also include applying the trained machine learning model to increase a spatial resolution of one or more images. Related methods and articles of manufacture are also disclosed.Type: ApplicationFiled: February 28, 2023Publication date: June 12, 2025Inventors: Anitha Priya KRISHNAN, Zhuang SONG, Richard Alan Duray CARANO
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Publication number: 20250166202Abstract: A method may include training a machine learning model to identify new lesions and/or enlarging lesions that developed within a multitemporal image input between a first timepoint and a second timepoint. The multitemporal image input includes a first image acquired at the first timepoint and a second image acquired at the second timepoint. The machine learning model is trained by at least generating a first representation of the multitemporal image input from the first timepoint to the second timepoint, a second representation of the multitemporal image input from the second timepoint to the first timepoint, a third representation of the multitemporal image input, and a lesion mask identifying the one or more new lesions and/or enlarging lesions. The method also includes applying the trained machine learning model to generate a lesion mask for a patient. Related methods and articles of manufacture are also disclosed.Type: ApplicationFiled: January 17, 2025Publication date: May 22, 2025Inventors: Anitha Priya KRISHNAN, Zhuang SONG, Richard Alan Duray CARANO
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Publication number: 20250157034Abstract: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.Type: ApplicationFiled: January 16, 2025Publication date: May 15, 2025Inventors: Daniel Irving GOLDEN, Fabien Rafael David BECKERS, John AXERIO-CILIES, Matthieu LE, Jesse LIEMAN-SIFRY, Anitha Priya KRISHNAN, Sean Patrick SALL, Hok Kan LAU, Matthew Joseph DIDONATO, Robert George NEWTON, Torin Arni TAERUM, Shek Bun LAW, Carla Rosa LEIBOWITZ, Angélique Sophie CALMON
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Publication number: 20250157033Abstract: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.Type: ApplicationFiled: January 16, 2025Publication date: May 15, 2025Inventors: Daniel Irving GOLDEN, Fabien Rafael David BECKERS, John AXERIO-CILIES, Matthieu LE, Jesse LIEMAN-SIFRY, Anitha Priya KRISHNAN, Sean Patrick SALL, Hok Kan LAU, Matthew Joseph DIDONATO, Robert George NEWTON, Torin Arni TAERUM, Shek Bun LAW, Carla Rosa LEIBOWITZ, Angélique Sophie CALMON
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Publication number: 20250131562Abstract: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.Type: ApplicationFiled: November 22, 2024Publication date: April 24, 2025Inventors: Daniel Irving GOLDEN, Fabien Rafael David BECKERS, John AXERIO-CILIES, Matthieu LE, Jesse LIEMAN-SIFRY, Anitha Priya KRISHNAN, Sean Patrick SALL, Hok Kan LAU, Matthew Joseph DIDONATO, Robert George NEWTON, Torin Arni TAERUM, Shek Bun LAW, Carla Rosa LEIBOWITZ, Angélique Sophie CALMON
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Patent number: 12183001Abstract: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.Type: GrantFiled: December 8, 2022Date of Patent: December 31, 2024Assignee: Arterys Inc.Inventors: Daniel Irving Golden, Fabien Rafael David Beckers, John Axerio-Cilies, Matthieu Le, Jesse Lieman-Sifry, Anitha Priya Krishnan, Sean Patrick Sall, Hok Kan Lau, Matthew Joseph Didonato, Robert George Newton, Torin Arni Taerum, Shek Bun Law, Carla Rosa Leibowitz, Angélique Sophie Calmon
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Publication number: 20230281809Abstract: Embodiments disclosed herein generally relate to connected machine learning models with joint training for lesion detection. Particularly, aspects of the present disclosure are directed to accessing a three-dimensional magnetic resonance imaging (MRI) image, wherein the three-dimensional MRI image depicts a region of a brain of a subject, wherein the region of the brain includes at least a first type of lesions and a second type of lesions; inputting the three-dimensional MRI image into a machine-learning model comprising a first convolutional neural network and a second convolutional neural network; generating a first segmentation mask for the first type of lesions using the first convolutional neural network that takes as input the three-dimensional MRI image; generating a second segmentation mask for the second type of lesions using the second convolutional neural network that takes as input the three-dimensional MRI image; and outputting the first segmentation mask and the second segmentation mask.Type: ApplicationFiled: February 27, 2023Publication date: September 7, 2023Applicants: GENENTECH, INC., HOFFMANN-LA ROCHE INC.Inventors: Zhuang Song, Nils Gustav Thomas Bengtsson, Richard Alan Duray Carano, David B. Clayton, Alexander James Stephen Champion De Crespigny, Laura Gaetano, Anitha Priya Krishnan
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Publication number: 20230206438Abstract: Embodiments disclosed herein generally relate to multi-arm machine learning models for lesion detection. Particularly, aspects of the present disclosure are directed to accessing a three-dimensional magnetic resonance imaging (MRI) images. Each of the three-dimensional MRI images depict a same volume of a brain of a subject. The volume of the brain includes at least part of one or more lesions. Each three-dimensional MRI image of the three-dimensional MRI images is processed using one or more corresponding encoder arms of a machine-learning model to generate an encoding of the three-dimensional MRI image. The encodings of the three-dimensional MRI images are concatenated to generate a concatenated representation. The concatenated representation is processed using a decoder arm of the machine-learning model to generate a prediction that identifies one or more portions of the volume of the brain predicted to depict at least part of a lesion.Type: ApplicationFiled: February 22, 2023Publication date: June 29, 2023Applicants: Genentech, Inc., Hoffman-La Roche Inc.Inventors: Zhuang Song, Nils Gustav Thomas Bengtsson, Richard Alan Duray Carano, David B. Clayton, Alexander James Stephen Champion De Crespigny, Laura Gaetano, Anitha Priya Krishnan
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Publication number: 20230106440Abstract: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.Type: ApplicationFiled: December 8, 2022Publication date: April 6, 2023Inventors: Daniel Irving GOLDEN, Fabien Rafael David BECKERS, John AXERIO-CILIES, Matthieu LE, Jesse LIEMAN-SIFRY, Anitha Priya KRISHNAN, Sean Patrick SALL, Hok Kan LAU, Matthew Joseph DIDONATO, Robert George NEWTON, Torin Arni TAERUM, Shek Bun LAW, Carla Rosa LEIBOWITZ, Angélique Sophie CALMON
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Patent number: 11551353Abstract: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.Type: GrantFiled: November 15, 2018Date of Patent: January 10, 2023Assignee: Arterys Inc.Inventors: Daniel Irving Golden, Fabien Rafael David Beckers, John Axerio-Cilies, Matthieu Le, Jesse Lieman-Sifry, Anitha Priya Krishnan, Sean Patrick Sall, Hok Kan Lau, Matthew Joseph Didonato, Robert George Newton, Torin Arni Taerum, Shek Bun Law, Carla Rosa Leibowitz, Angélique Sophie Calmon
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Publication number: 20200380675Abstract: Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.Type: ApplicationFiled: November 15, 2018Publication date: December 3, 2020Inventors: Daniel Irving GOLDEN, Fabien Rafael David BECKERS, John AXERIO-CILIES, Matthieu LE, Jesse LIEMAN-SIFRY, Anitha Priya KRISHNAN, Sean Patrick SALL, Hok Kan LAU, Matthew Joseph DIDONATO, Robert George NEWTON, Torin Arni TAERUM, Shek Bun LAW, Carla Rosa LEIBOWITZ, Angélique Sophie CALMON
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Patent number: 9880885Abstract: A method in a server end station is described. The method includes performing an iteration of a rebalancing computation by selecting a set of one or more service sets for rebalancing, wherein the selecting the set of one or more service sets is based on service set constraints and host constraints; generating candidate solutions, wherein each candidate solution includes a randomized one-to-one mapping of each of the service sets to one of the hosts; performing one or more crossover operations on the candidate solutions; performing one or more mutation operations on the additional candidate solutions; selecting as a solution one of the candidate solutions that has a best fitness score, wherein a fitness score for a candidate solution is calculated based on the distribution of resources resulting from and number of migrations needed for the candidate solution; and repeating the iteration of the rebalancing computation an additional number of times.Type: GrantFiled: April 17, 2015Date of Patent: January 30, 2018Assignee: Telefonaktiebolaget LM Ericsson (Publ)Inventors: Priya Krishnan Sundararajan, Eugen Feller, Julien Forgeat
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Patent number: 9612766Abstract: Implementations described and claimed herein provide systems and methods for estimating migration progress. In one implementation, a target file system is initialized to which to migrate existing data from a source file system. An initial amount of data to be migrated to the target file system is estimated based on an examination of in-use space at a root of the source file system. Any mount points for nested file systems in the source file system are identified. An amount of data for each of the nested file systems is estimated based on an examination of in-use space at the mount point for the nested file system. An estimated total amount of data to be migrated from the source file system to the target file system is determined based on the initial amount of data to be migrated and the amount of data for each of the nested file systems.Type: GrantFiled: December 19, 2014Date of Patent: April 4, 2017Assignee: ORACLE INTERNATIONAL CORPORATIONInventors: Timothy Haley, Mark Maybee, Priya Krishnan
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Publication number: 20160226789Abstract: A method in a server end station is described. The method includes performing an iteration of a rebalancing computation by selecting a set of one or more service sets for rebalancing, wherein the selecting the set of one or more service sets is based on service set constraints and host constraints; generating candidate solutions, wherein each candidate solution includes a randomized one-to-one mapping of each of the service sets to one of the hosts; performing one or more crossover operations on the candidate solutions; performing one or more mutation operations on the additional candidate solutions; selecting as a solution one of the candidate solutions that has a best fitness score, wherein a fitness score for a candidate solution is calculated based on the distribution of resources resulting from and number of migrations needed for the candidate solution; and repeating the iteration of the rebalancing computation an additional number of times.Type: ApplicationFiled: April 17, 2015Publication date: August 4, 2016Inventors: Priya Krishnan SUNDARARAJAN, Eugen FELLER, Julien FORGEAT
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Publication number: 20160179435Abstract: Implementations described and claimed herein provide systems and methods for estimating migration progress. In one implementation, a target file system is initialized to which to migrate existing data from a source file system. An initial amount of data to be migrated to the target file system is estimated based on an examination of in-use space at a root of the source file system. Any mount points for nested file systems in the source file system are identified. An amount of data for each of the nested file systems is estimated based on an examination of in-use space at the mount point for the nested file system. An estimated total amount of data to be migrated from the source file system to the target file system is determined based on the initial amount of data to be migrated and the amount of data for each of the nested file systems.Type: ApplicationFiled: December 19, 2014Publication date: June 23, 2016Applicant: Oracle International CorporationInventors: Timothy Haley, Mark Maybee, Priya Krishnan