Patents by Inventor Xiangxue Wang

Xiangxue Wang 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).

  • Publication number: 20250014326
    Abstract: Methods, computer-program products and systems are provided to perform actions including: receiving an image and displaying the image using a graphical user interface; receiving at least one first image annotation provided by a user via the graphical user interface; producing a first segmented image using a deep learning model, wherein the deep learning model uses the digital pathology image and the at least one first image annotation; and displaying the first segmented image using the graphical user interface; receiving at least one second image annotation provided by the user via the graphical user interface; producing a second segmented image using the deep learning model, wherein the deep learning model uses the digital pathology image, the at least one first image annotation, and the at least one second image annotation; and displaying the second segmented image using the graphical user interface.
    Type: Application
    Filed: September 17, 2024
    Publication date: January 9, 2025
    Applicant: VENTANA MEDICAL SYSTEMS, INC.
    Inventors: Qinle BA, Jim F. Martin, Satarupa Mukherjee, Xiangxue Wang, Mohammadhassan Izady Yazdanabadi
  • Patent number: 12086985
    Abstract: A scoring functions is developed and used for identifying patients who might be responsive to a PD-1 axis directed therapy. The scoring functions are obtained by extracting features from multiplex-stained sections, selecting features that correlate with response to the therapy using a feature selection function, and fitting one or more of the selected features to a plurality of candidate scoring functions. A candidate scoring function showing the desired balance between predictive sensitivity and specificity may then selected for incorporation into a scoring system that includes at least an image analysis system.
    Type: Grant
    Filed: March 30, 2021
    Date of Patent: September 10, 2024
    Assignees: Memorial Sloan Kettering Cancer Center
    Inventors: Mehrnoush Khojasteh, Jim F. Martin, Lidija Pestic-Dragovich, Lei Tang, Xiangxue Wang, Wenjun Zhang, Robert Anders, Luis Diaz
  • Patent number: 11861836
    Abstract: The degree of differentiation of a cell in tissue is precisely determined. An estimating device (1) includes: a binarizing section (41) configured to generate binarized images from an image obtained by capturing an image of tissue; a Betti number calculating section (42) configured to calculate, for each binarized image, (i) the number of hole-shaped regions (b1) each surrounded by pixels of a first pixel value and each composed of pixels of a second pixel value, (ii) the number of connected regions each composed of the pixels of the first pixel value connected together, and (iii) a ratio (R) between (i) and (ii); a statistic calculating section (43) configured to calculate statistics of the calculated numbers (b1, b0) and ratio (R); and an estimating section (44) configured to feed input data including the calculated statistics to a trained estimating model to output the degree of differentiation of the cell in tissue.
    Type: Grant
    Filed: May 24, 2021
    Date of Patent: January 2, 2024
    Assignee: APSAM Imaging Corp.
    Inventors: Kazuaki Nakane, Chaoyang Yan, Xiangxue Wang, Yao Fu, Haoda Lu, Xiangshan Fan, Michael D. Feldman, Anant Madabhushi, Jun Xu
  • Publication number: 20230307132
    Abstract: Methods and systems can include: accessing a digital pathology image; generating, using a first machine-learning model, a segmented image that identifies at least: a predicted diseased region and a background region in the digital pathology image; detecting depictions of a set of cells in the digital pathology image; generating, using a second machine-learning model, a cell classification for each cell of the set of cells, wherein the cell classification is selected from a set of potential classifications that indicate which, if any, of a set of biomarkers are expressed in the cell; detecting that a subset of the set of cells are within the background region; and updating the cell classification for each cell of at least some cells in the subset to be a background classification that was not included in the set of potential classifications.
    Type: Application
    Filed: March 22, 2023
    Publication date: September 28, 2023
    Applicant: VENTANA MEDICAL SYSTEMS, INC.
    Inventors: Qinle Ba, Jim F. Martin, Satarupa Mukherjee, Yao Nie, Xiangxue Wang, Mohammadhassan Izady Yazdanabadi
  • Publication number: 20220207738
    Abstract: The degree of differentiation of a cell in tissue is precisely determined. An estimating device (1) includes: a binarizing section (41) configured to generate binarized images from an image obtained by capturing an image of tissue; a Betti number calculating section (42) configured to calculate, for each binarized image, (i) the number of hole-shaped regions (b1) each surrounded by pixels of a first pixel value and each composed of pixels of a second pixel value, (ii) the number of connected regions each composed of the pixels of the first pixel value connected together, and (iii) a ratio (R) between (i) and (ii); a statistic calculating section (43) configured to calculate statistics of the calculated numbers (b1, b0) and ratio (R); and an estimating section (44) configured to feed input data including the calculated statistics to a trained estimating model to output the degree of differentiation of the cell in tissue.
    Type: Application
    Filed: May 24, 2021
    Publication date: June 30, 2022
    Inventors: Kazuaki NAKANE, Chaoyang YAN, Xiangxue WANG, Yao FU, Haoda LU, Xiangshan FAN, Michael D. FELDMAN, Anant MADABHUSHI, Jun XU
  • Publication number: 20210374962
    Abstract: A scoring functions is developed and used for identifying patients who might be responsive to a PD-1 axis directed therapy. The scoring functions are obtained by extracting features from multiplex-stained sections, selecting features that correlate with response to the therapy using a feature selection function, and fitting one or more of the selected features to a plurality of candidate scoring functions. A candidate scoring function showing the desired balance between predictive sensitivity and specificity may then selected for incorporation into a scoring system that includes at least an image analysis system.
    Type: Application
    Filed: March 30, 2021
    Publication date: December 2, 2021
    Inventors: Mehrnoush KHOJASTEH, Jim F. MARTIN, Lidija PESTIC-DRAGOVICH, Lei TANG, Xiangxue WANG, Wenjun ZHANG, Robert ANDERS, Luis DIAZ
  • Patent number: 11107583
    Abstract: Embodiments discussed herein facilitate generation of a prognosis for a medical condition based on determination of one or more histomorphometric features for tiles of a whole slide image (WSI) that have been identified as the most prognostically significant tiles of the WSI. A first set of embodiments discussed herein relates to training of a fully convolutional network (FCN) to determine the prognostic significance of pixels of a WSI. A second set of embodiments discussed herein relates to determination of a prognosis based on analysis of regions identified as the most prognostically significant by a trained FCN.
    Type: Grant
    Filed: March 20, 2019
    Date of Patent: August 31, 2021
    Assignee: Case Western Reserve University
    Inventors: Anant Madabhushi, Xiangxue Wang
  • Patent number: 11055844
    Abstract: Embodiments access a digitized image of tissue demonstrating non-small cell lung cancer (NSCLC), the tissue including a plurality of cellular nuclei; segment the plurality of cellular nuclei represented in the digitized image; extract a set of nuclear radiomic features from the plurality of segmented cellular nuclei; generate at least one nuclear cell graph (CG) based on the plurality of segmented nuclei; compute a set of CG features based on the nuclear CG; provide the set of nuclear radiomic features and the set of CG features to a machine learning classifier; receive, from the machine learning classifier, a probability that the tissue will respond to immunotherapy, based, at least in part, on the set of nuclear radiomic features and the set of CG features; generate a classification of the tissue as a responder or non-responder based on the probability; and display the classification.
    Type: Grant
    Filed: February 21, 2019
    Date of Patent: July 6, 2021
    Assignees: Case Western Reserve University, The Cleveland Clinic Foundation
    Inventors: Anant Madabhushi, Xiangxue Wang, Cristian Barrera, Vamsidhar Velcheti
  • Patent number: 10956795
    Abstract: Embodiments predict early stage NSCLC recurrence, and include an image acquisition circuit configured to access an image of a region of tissue demonstrating early-stage NSCLC including a plurality of cellular nuclei; a nuclei detecting and segmentation circuit configured to detect a member of the plurality; and classify the member as a tumor infiltrating lymphocyte (TIL) nucleus or non-TIL nucleus; a spatial TIL feature circuit configured to extract spatial TIL features from the plurality, the spatial TIL features including a first subset of features based on the spatial arrangement of TIL nuclei, and a second subset of features based on the spatial relationship between TIL nuclei and non-TIL nuclei; and an NSCLC recurrence classification circuit configured to compute a probability that region will experience recurrence based on the spatial TIL features; and generate a classification of the region as likely or unlikely to experience recurrence based on the probability.
    Type: Grant
    Filed: August 24, 2018
    Date of Patent: March 23, 2021
    Assignees: Case Western Reserve University, The Cleveland Clinic Foundation
    Inventors: Anant Madabhushi, Xiangxue Wang, Vamsidhar Velcheti
  • Patent number: 10902256
    Abstract: Embodiments include controlling a processor to perform operations, the operations comprising: accessing a digitized image of a region of tissue demonstrating non-small cell lung cancer (NSCLC), detecting a member of a plurality of cellular nuclei represented in the image; classifying the member of the plurality of cellular nuclei as a tumor infiltrating lymphocyte (TIL) nucleus or non-TIL nucleus; extracting spatial TIL features from the plurality of cellular nuclei, including a first subset of features based on the spatial arrangement of TIL nuclei, and a second, different subset of features based on the spatial relationship between TIL nuclei and non-TIL nuclei; generating a set of graph interplay features based on the set of spatial TIL features; providing the set of graph interplay features to a machine learning classifier; receiving, from the machine learning classifier, a probability that the region of tissue will respond to immunotherapy, based, at least in part, on the set of graph interplay features;
    Type: Grant
    Filed: February 15, 2019
    Date of Patent: January 26, 2021
    Assignee: Case Western Reserve University
    Inventors: Anant Madabhushi, Xiangxue Wang, Cristian Barrera, Vamsidhar Velcheti
  • Patent number: 10846367
    Abstract: Embodiments predict early stage NSCLC recurrence, and include processors configured to access a pathology image of a region of tissue demonstrating early stage NSCLC; extract a set of pathomic features from the pathology image; access a radiological image of the region of tissue; extract a set of radiomic features from the radiological image; generate a combined feature set that includes at least one member of the set of pathomic features, and at least one member of the set of radiomic features; compute a probability that the region of tissue will experience NSCLC recurrence based, at least in part, on the combined feature set; and classify the region of tissue as recurrent or non-recurrent based, at least in part, on the probability. Embodiments may display the classification, or generate a personalized treatment plan based on the classification.
    Type: Grant
    Filed: September 14, 2018
    Date of Patent: November 24, 2020
    Assignee: Case Western Reserve University University
    Inventors: Anant Madabhushi, Xiangxue Wang, Pranjal Vaidya, Vamsidhar Velcheti
  • Publication number: 20190295721
    Abstract: Embodiments discussed herein facilitate generation of a prognosis for a medical condition based on determination of one or more histomorphometric features for tiles of a whole slide image (WSI) that have been identified as the most prognostically significant tiles of the WSI. A first set of embodiments discussed herein relates to training of a fully convolutional network (FCN) to determine the prognostic significance of pixels of a WSI. A second set of embodiments discussed herein relates to determination of a prognosis based on analysis of regions identified as the most prognostically significant by a trained FCN.
    Type: Application
    Filed: March 20, 2019
    Publication date: September 26, 2019
    Inventors: Anant Madabhushi, Xiangxue Wang
  • Publication number: 20190259154
    Abstract: Embodiments access a digitized image of tissue demonstrating non-small cell lung cancer (NSCLC), the tissue including a plurality of cellular nuclei; segment the plurality of cellular nuclei represented in the digitized image; extract a set of nuclear radiomic features from the plurality of segmented cellular nuclei; generate at least one nuclear cell graph (CG) based on the plurality of segmented nuclei; compute a set of CG features based on the nuclear CG; provide the set of nuclear radiomic features and the set of CG features to a machine learning classifier; receive, from the machine learning classifier, a probability that the tissue will respond to immunotherapy, based, at least in part, on the set of nuclear radiomic features and the set of CG features; generate a classification of the tissue as a responder or non-responder based on the probability; and display the classification.
    Type: Application
    Filed: February 21, 2019
    Publication date: August 22, 2019
    Inventors: Anant Madabhushi, Xiangxue Wang, Cristian Barrera, Vamsidhar Velcheti
  • Publication number: 20190258855
    Abstract: Embodiments include controlling a processor to perform operations, the operations comprising: accessing a digitized image of a region of tissue demonstrating non-small cell lung cancer (NSCLC), detecting a member of a plurality of cellular nuclei represented in the image; classifying the member of the plurality of cellular nuclei as a tumor infiltrating lymphocyte (TIL) nucleus or non-TIL nucleus; extracting spatial TIL features from the plurality of cellular nuclei, including a first subset of features based on the spatial arrangement of TIL nuclei, and a second, different subset of features based on the spatial relationship between TIL nuclei and non-TIL nuclei; generating a set of graph interplay features based on the set of spatial TIL features; providing the set of graph interplay features to a machine learning classifier; receiving, from the machine learning classifier, a probability that the region of tissue will respond to immunotherapy, based, at least in part, on the set of graph interplay features;
    Type: Application
    Filed: February 15, 2019
    Publication date: August 22, 2019
    Inventors: Anant Madabhushi, Xiangxue Wang, Cristian Barrera, Vamsidhar Velcheti
  • Publication number: 20190087532
    Abstract: Embodiments predict early stage NSCLC recurrence, and include processors configured to access a pathology image of a region of tissue demonstrating early stage NSCLC; extract a set of pathomic features from the pathology image; access a radiological image of the region of tissue; extract a set of radiomic features from the radiological image; generate a combined feature set that includes at least one member of the set of pathomic features, and at least one member of the set of radiomic features; compute a probability that the region of tissue will experience NSCLC recurrence based, at least in part, on the combined feature set; and classify the region of tissue as recurrent or non-recurrent based, at least in part, on the probability. Embodiments may display the classification, or generate a personalized treatment plan based on the classification.
    Type: Application
    Filed: September 14, 2018
    Publication date: March 21, 2019
    Inventors: Anant Madabhushi, Xiangxue Wang, Pranjal Vaidya, Vamsidhar Velcheti
  • Publication number: 20190087693
    Abstract: Embodiments predict early stage NSCLC recurrence, and include an image acquisition circuit configured to access an image of a region of tissue demonstrating early-stage NSCLC including a plurality of cellular nuclei; a nuclei detecting and segmentation circuit configured to detect a member of the plurality; and classify the member as a tumor infiltrating lymphocyte (TIL) nucleus or non-TIL nucleus; a spatial TIL feature circuit configured to extract spatial TIL features from the plurality, the spatial TIL features including a first subset of features based on the spatial arrangement of TIL nuclei, and a second subset of features based on the spatial relationship between TIL nuclei and non-TIL nuclei; and an NSCLC recurrence classification circuit configured to compute a probability that region will experience recurrence based on the spatial TIL features; and generate a classification of the region as likely or unlikely to experience recurrence based on the probability.
    Type: Application
    Filed: August 24, 2018
    Publication date: March 21, 2019
    Inventors: Anant Madabhushi, Xiangxue Wang, Vamsidhar Velcheti
  • Patent number: 10078895
    Abstract: Methods and apparatus predict non-small cell lung cancer (NSCLC) recurrence using radiomic features extracted from digitized hematoxylin and eosin (H&E) stained slides of a region of tissue demonstrating NSCLC. One example apparatus includes an image acquisition circuit that acquires an image of a region of tissue demonstrating NSCLC, a segmentation circuit that segments a cellular nucleus from the image, a feature extraction circuit that extracts a set of features from the image, a tumor infiltrating lymphocyte (TIL) identification circuit that classifies the segmented nucleus as a TIL or non-TIL, a graphing circuit that constructs a TIL graph and computes a set of TIL graph statistical features, and a classification circuit that computes a probability that the region will experience NSCLC recurrence. The classification circuit may compute a quantitative continuous image-based risk score based on the probability or the image. A treatment plan may be provided based on the risk score.
    Type: Grant
    Filed: December 23, 2016
    Date of Patent: September 18, 2018
    Assignee: Case Western Reserve University
    Inventors: Anant Madabhushi, Xiangxue Wang, Vamsidhar Velcheti, German Corredor Prada
  • Patent number: 10049770
    Abstract: Methods, apparatus, and other embodiments associated with predicting non-small cell lung cancer (NSCLC) patient response to adjuvant chemotherapy therapy using radiomic features extracted from digitized hematoxylin and eosin (H&E) stained slides of a region of tissue demonstrating NSCLC. One example apparatus includes an image acquisition circuit that acquires an H&E image of a region of tissue demonstrating NSCLC pathology, a segmentation circuit that segments a region of interest (ROI) from the diagnostic radiological image, a feature extraction that extracts a set of discriminative features from the ROI, and a classification circuit that generates a probability that the ROI will experience NSCLC recurrence. The classification circuit may compute a quantitative continuous image-based risk score based on the probability or the image. A prognosis or treatment plan may be provided based on the quantitative continuous image-based risk score.
    Type: Grant
    Filed: December 23, 2016
    Date of Patent: August 14, 2018
    Assignee: Case Western Reserve University
    Inventors: Anant Madabhushi, Xiangxue Wang, Vamsidhar Velcheti
  • Publication number: 20170193175
    Abstract: Methods, apparatus, and other embodiments associated with predicting non-small cell lung cancer (NSCLC) patient response to adjuvant chemotherapy therapy using radiomic features extracted from digitized hematoxylin and eosin (H&E) stained slides of a region of tissue demonstrating NSCLC. One example apparatus includes an image acquisition circuit that acquires an H&E image of a region of tissue demonstrating NSCLC pathology, a segmentation circuit that segments a region of interest (ROI) from the diagnostic radiological image, a feature extraction that extracts a set of discriminative features from the ROI, and a classification circuit that generates a probability that the ROI will experience NSCLC recurrence. The classification circuit may compute a quantitative continuous image-based risk score based on the probability or the image. A prognosis or treatment plan may be provided based on the quantitative continuous image-based risk score.
    Type: Application
    Filed: December 23, 2016
    Publication date: July 6, 2017
    Inventors: Anant Madabhushi, Xiangxue Wang, Vamsidhar Velcheti
  • Publication number: 20170193657
    Abstract: Methods and apparatus predict non-small cell lung cancer (NSCLC) recurrence using radiomic features extracted from digitized hematoxylin and eosin (H&E) stained slides of a region of tissue demonstrating NSCLC. One example apparatus includes an image acquisition circuit that acquires an image of a region of tissue demonstrating NSCLC, a segmentation circuit that segments a cellular nucleus from the image, a feature extraction circuit that extracts a set of features from the image, a tumor infiltrating lymphocyte (TIL) identification circuit that classifies the segmented nucleus as a TIL or non-TIL, a graphing circuit that constructs a TIL graph and computes a set of TIL graph statistical features, and a classification circuit that computes a probability that the region will experience NSCLC recurrence. The classification circuit may compute a quantitative continuous image-based risk score based on the probability or the image. A treatment plan may be provided based on the risk score.
    Type: Application
    Filed: December 23, 2016
    Publication date: July 6, 2017
    Inventors: Anant Madabhushi, Xiangxue Wang, Vamsidhar Velcheti, German Corredor Prada