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US Patent 11783476 System and method for analyzing three-dimensional image data

Patent 11783476 was granted and assigned to DEEPHEALTH, INC. on October, 2023 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
DEEPHEALTH, INC.
DEEPHEALTH, INC.
Current Assignee
DEEPHEALTH, INC.
DEEPHEALTH, INC.
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
11783476
Patent Inventor Names
William Lotter
Date of Patent
October 10, 2023
Patent Application Number
17079957
Date Filed
October 26, 2020
Patent Citations
‌
US Patent 7577282 Image handling and display in X-ray mammography and tomosynthesis
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US Patent 7616801 Image handling and display in x-ray mammography and tomosynthesis
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US Patent 7640051 Systems and methods for automated diagnosis and decision support for breast imaging
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US Patent 7668352 Communicating computer-aided detection results in a standards-based medical imaging environment
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US Patent 7672494 Processing medical image information to detect anatomical abnormalities
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US Patent 7702142 Matching geometry generation and display of mammograms and tomosynthesis images
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US Patent 7760853 Full field mammography with tissue exposure control, tomosynthesis, and dynamic field of view processing
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US Patent 7760924 System and method for generating a 2D image from a tomosynthesis data set
...
Patent Primary Examiner
‌
Edward F. Urban
CPC Code
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G06T 5/40
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G06T 2207/10112
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G06T 2207/20084
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G06T 2207/30068
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G16H 50/20
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G06V 10/25
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G06V 20/647
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G06V 2201/03
...
Patent abstract

The present disclosure provides a method for determining a malignancy likelihood score for breast tissue of a patient. The method includes receiving a plurality of two-dimensional images of the breast tissue, the two-dimensional images being derived from a three-dimensional image of the breast tissue, for each two-dimensional image, providing the two-dimensional image to a first model including a first trained neural network, and receiving a number of indicators from the first model, each indicator being associated with a two-dimensional image included in the plurality of two-dimensional images, generating a synthetic two-dimensional image based on the number of indicators and at least one of the plurality of two-dimensional images, providing the synthetic two-dimensional image to a second model including a second trained neural network, receiving a malignancy likelihood score from the second model, and outputting a report including the malignancy likelihood score to at least one of a memory or a display.

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