Patents by Inventor Saad A. Sirohey
Saad A. Sirohey 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: 11704804Abstract: Techniques are described for domain adaptation of image processing models using post-processing model correction According to an embodiment, a method comprises training, by a system operatively coupled to a processor, a post-processing model to correct an image-based inference output of a source image processing model that results from application of the source image processing model to a target image from a target domain that differs from a source domain, wherein the source image processing model was trained on source images from the source domain. In one or more implementations, the source imaging processing model comprises an organ segmentation model and the post-processing model can comprise a shape-autoencoder. The method further comprises applying, by the system, the source image processing model and the post-processing model to target images from the target domain to generate optimized image-based inference outputs for the target images.Type: GrantFiled: June 12, 2020Date of Patent: July 18, 2023Assignee: GE PRECISION HEALTHCARE LLCInventors: Sidharth Abrol, Bipul Das, Sandeep Dutta, Saad A. Sirohey
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Publication number: 20210312674Abstract: Techniques are described for domain adaptation of image processing models using post-processing model correction According to an embodiment, a method comprises training, by a system operatively coupled to a processor, a post-processing model to correct an image-based inference output of a source image processing model that results from application of the source image processing model to a target image from a target domain that differs from a source domain, wherein the source image processing model was trained on source images from the source domain. In one or more implementations, the source imaging processing model comprises an organ segmentation model and the post-processing model can comprise a shape-autoencoder. The method further comprises applying, by the system, the source image processing model and the post-processing model to target images from the target domain to generate optimized image-based inference outputs for the target images.Type: ApplicationFiled: June 12, 2020Publication date: October 7, 2021Inventors: Sidharth Abrol, Bipul Das, Sandeep Dutta, Saad A. Sirohey
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Patent number: 11003988Abstract: Methods and apparatus for deep learning-based system design improvement are provided. An example system design engine apparatus includes a deep learning network (DLN) model associated with each component of a target system to be emulated, each DLN model to be trained using known input and known output, wherein the known input and known output simulate input and output of the associated component of the target system, and wherein each DLN model is connected as each associated component to be emulated is connected in the target system to form a digital model of the target system. The example apparatus also includes a model processor to simulate behavior of the target system and/or each component of the target system to be emulated using the digital model to generate a recommendation regarding a configuration of a component of the target system and/or a structure of the component of the target system.Type: GrantFiled: November 23, 2016Date of Patent: May 11, 2021Assignee: General Electric CompanyInventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey
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Patent number: 10896352Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.Type: GrantFiled: November 27, 2019Date of Patent: January 19, 2021Assignee: General Electric CompanyInventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey, Xin Wang, Zhye Yin, Bruno De Man
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Patent number: 10628943Abstract: Methods and apparatus for improved deep learning for image acquisition are provided. An imaging system configuration apparatus includes a training learning device including a first processor to implement a first deep learning network (DLN) to learn a first set of imaging system configuration parameters based on a first set of inputs from a plurality of prior image acquisitions to configure at least one imaging system for image acquisition, the training learning device to receive and process feedback including operational data from the plurality of image acquisitions by the at least one imaging system. The example apparatus includes a deployed learning device including a second processor to implement a second DLN, the second DLN generated from the first DLN of the training learning device, the deployed learning device configured to provide a second imaging system configuration parameter to the imaging system in response to receiving a second input for image acquisition.Type: GrantFiled: October 9, 2018Date of Patent: April 21, 2020Assignee: General Electric CompanyInventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey
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Publication number: 20200097773Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.Type: ApplicationFiled: November 27, 2019Publication date: March 26, 2020Inventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey, Xin Wang, Zhye Yin, Bruno DeMan
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Patent number: 10565477Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.Type: GrantFiled: July 15, 2019Date of Patent: February 18, 2020Assignee: General Electric CompanyInventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey, Xin Wang, Zhye Yin, Bruno De Man
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Publication number: 20190340470Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.Type: ApplicationFiled: July 15, 2019Publication date: November 7, 2019Inventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey, Xin Wang, Zhye Yin, Bruno De Man
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Patent number: 10438354Abstract: Methods and apparatus for monitoring and improving imaging system operation are provided. An example apparatus includes a first deployed deep learning network (DLN) which operates with an acquisition engine to generate an imaging device configuration. The example apparatus includes a second deployed DLN which operates with a reconstruction engine based on acquired image data. The example apparatus includes a first assessment engine with a third deployed DLN. The assessment engine receives output from at least one of the acquisition engine or the reconstruction engine to assess operation of the respective at least one of the acquisition engine or the reconstruction engine and to provide feedback to the respective at least one of the acquisition engine or the reconstruction engine. The first deployed DLN and the second deployed DLN are generated and deployed from first and second training DLNS, respectively.Type: GrantFiled: March 20, 2019Date of Patent: October 8, 2019Assignee: General Electric CompanyInventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey
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Publication number: 20190220975Abstract: Methods and apparatus for monitoring and improving imaging system operation are provided. An example apparatus includes a first deployed deep learning network (DLN) which operates with an acquisition engine to generate an imaging device configuration. The example apparatus includes a second deployed DLN which operates with a reconstruction engine based on acquired image data. The example apparatus includes a first assessment engine with a third deployed DLN. The assessment engine receives output from at least one of the acquisition engine or the reconstruction engine to assess operation of the respective at least one of the acquisition engine or the reconstruction engine and to provide feedback to the respective at least one of the acquisition engine or the reconstruction engine. The first deployed DLN and the second deployed DLN are generated and deployed from first and second training DLNS, respectively.Type: ApplicationFiled: March 20, 2019Publication date: July 18, 2019Inventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey
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Patent number: 10354171Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.Type: GrantFiled: September 10, 2018Date of Patent: July 16, 2019Assignee: General Electric CompanyInventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey, Xin Wang, Zhye Yin, Bruno De Man
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Patent number: 10299751Abstract: A computed tomography (CT) imaging system includes a CT imaging unit, a display unit and at least one processor. The CT imaging unit includes an X-ray source and a CT detector. The at least one processor is operably coupled to the imaging unit and the display unit, and is configured to: acquire at least three phases of CT imaging information via the CT imaging unit; determine timing information for imaging intensity of blood vessels represented in the CT imaging information; assign corresponding colors to the blood vessels based on the timing information; reconstruct an image using the CT imaging information from the at least three phases, wherein the blood vessels depicted in the reconstructed image are represented using the corresponding colors based on the timing information; and display the image on the display unit.Type: GrantFiled: March 9, 2017Date of Patent: May 28, 2019Assignee: General Electric CompanyInventors: Vincent Adam, Saad Sirohey, Guillaume Neveux, Yannick Le Berre, Maud Bonnard
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Patent number: 10242443Abstract: Methods and apparatus for monitoring and improving imaging system operation are provided. An example apparatus includes a first deployed deep learning network (DLN) which operates with an acquisition engine to generate an imaging device configuration. The example apparatus includes a second deployed DLN which operates with a reconstruction engine based on acquired image data. The example apparatus includes a first assessment engine with a third deployed DLN. The assessment engine receives output from at least one of the acquisition engine or the reconstruction engine to assess operation of the respective at least one of the acquisition engine or the reconstruction engine and to provide feedback to the respective at least one of the acquisition engine or the reconstruction engine. The first deployed DLN and the second deployed DLN are generated and deployed from first and second training DLNS, respectively.Type: GrantFiled: November 23, 2016Date of Patent: March 26, 2019Assignee: General Electric CompanyInventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey
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Publication number: 20190050987Abstract: Methods and apparatus for improved deep learning for image acquisition are provided. An imaging system configuration apparatus includes a training learning device including a first processor to implement a first deep learning network (DLN) to learn a first set of imaging system configuration parameters based on a first set of inputs from a plurality of prior image acquisitions to configure at least one imaging system for image acquisition, the training learning device to receive and process feedback including operational data from the plurality of image acquisitions by the at least one imaging system. The example apparatus includes a deployed learning device including a second processor to implement a second DLN, the second DLN generated from the first DLN of the training learning device, the deployed learning device configured to provide a second imaging system configuration parameter to the imaging system in response to receiving a second input for image acquisition.Type: ApplicationFiled: October 9, 2018Publication date: February 14, 2019Inventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey
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Publication number: 20190026608Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.Type: ApplicationFiled: September 10, 2018Publication date: January 24, 2019Inventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey, Xin Wang, Zhye Yin, Bruno De Man
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Patent number: 10127659Abstract: Methods and apparatus for improved deep learning for image acquisition are provided. An imaging system configuration apparatus includes a training learning device including a first processor to implement a first deep learning network (DLN) to learn a first set of imaging system configuration parameters based on a first set of inputs from a plurality of prior image acquisitions to configure at least one imaging system for image acquisition, the training learning device to receive and process feedback including operational data from the plurality of image acquisitions by the at least one imaging system. The example apparatus includes a deployed learning device including a second processor to implement a second DLN, the second DLN generated from the first DLN of the training learning device, the deployed learning device configured to provide a second imaging system configuration parameter to the imaging system in response to receiving a second input for image acquisition.Type: GrantFiled: November 23, 2016Date of Patent: November 13, 2018Assignee: General Electric CompanyInventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey
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Patent number: 10074038Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.Type: GrantFiled: November 23, 2016Date of Patent: September 11, 2018Assignee: General Electric CompanyInventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey, Xin Wang, Zhye Yin, Bruno De Man
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Publication number: 20180144465Abstract: Methods and apparatus for monitoring and improving imaging system operation are provided. An example apparatus includes a first deployed deep learning network (DLN) which operates with an acquisition engine to generate an imaging device configuration. The example apparatus includes a second deployed DLN which operates with a reconstruction engine based on acquired image data. The example apparatus includes a first assessment engine with a third deployed DLN. The assessment engine receives output from at least one of the acquisition engine or the reconstruction engine to assess operation of the respective at least one of the acquisition engine or the reconstruction engine and to provide feedback to the respective at least one of the acquisition engine or the reconstruction engine. The first deployed DLN and the second deployed DLN are generated and deployed from first and second training DLNS, respectively.Type: ApplicationFiled: November 23, 2016Publication date: May 24, 2018Inventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey
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Publication number: 20180144466Abstract: Methods and apparatus for improved deep learning for image acquisition are provided. An imaging system configuration apparatus includes a training learning device including a first processor to implement a first deep learning network (DLN) to learn a first set of imaging system configuration parameters based on a first set of inputs from a plurality of prior image acquisitions to configure at least one imaging system for image acquisition, the training learning device to receive and process feedback including operational data from the plurality of image acquisitions by the at least one imaging system. The example apparatus includes a deployed learning device including a second processor to implement a second DLN, the second DLN generated from the first DLN of the training learning device, the deployed learning device configured to provide a second imaging system configuration parameter to the imaging system in response to receiving a second input for image acquisition.Type: ApplicationFiled: November 23, 2016Publication date: May 24, 2018Inventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey
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Publication number: 20180144214Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.Type: ApplicationFiled: November 23, 2016Publication date: May 24, 2018Inventors: Jiang Hsieh, Gopal Avinash, Saad Sirohey, Xin Wang, Zhye Yin, Bruno De Man