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US 10,255,997 B2Granted
Medical analytics system
AssigneeMindshare Medical, Inc.
InventorsMichael E. Calhoun, Samir Chowdhury, Ilya Goldberg
Life of this patent
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Abstract
Systems and methods of a medical analytics system are described herein. The medical analytics system can include a machine learning model for processing patient tissue images for either training the machine learning model or for clinical use, such as providing information for assisting a clinician with at least diagnosing a disease or condition of a patient. Implementations of the medical analytics system can further include a user interface that is configured to allow a user to interact with a patient image for assisting with diagnosing at least a part of the tissue captured in the patient image.
Claims18
A system, comprising:
- at least one processor;
- and
- at least one memory including program code which when executed by the at least one processor provides operations comprising:
- projecting a three-dimensional image of a patient tissue into a plurality of two-dimensional grayscale images;
- applying at least one transformation algorithm to a first set of two-dimensional grayscale images to generate a first set of transformed two-dimensional grayscale images;
- applying at least one feature algorithm to at least one two-dimensional grayscale image and to each transformed two-dimensional grayscale images of the first set of transformed two-dimensional grayscale images;
- generating, based on the applying of the at least one feature algorithm to at least one two-dimensional grayscale image and to each transformed two-dimensional grayscale images, a plurality of feature values comprising a feature vector;
- projecting the three-dimensional image into a two-dimensional color image;
- applying at least one color transformation algorithm to a first set of two-dimensional color images to generate a first set of color-transformed two-dimensional grayscale images;
- applying at least one feature algorithm to at least one two-dimensional color image and to each color-transformed two-dimensional grayscale images;
- generating, based on the applying of the at least one feature algorithm to the at least one two-dimensional color image and to each of the transformed two-dimensional color images, a plurality of color feature values comprising the feature vector;
- collecting patient information;
- generating, based on the collected patient information, one or more patient values comprising the feature vector;
- training a machine learning model based on the feature vector and an associated diagnosis of the patient tissue, the machine learning model comprising a classifier having a weighted value assigned to each of the plurality of feature values, the plurality of color feature values, and the patient value.
11 more elements
A computer-implemented method, comprising:
- projecting a three-dimensional image of a patient tissue into a plurality of two-dimensional grayscale images;
- applying at least one transformation algorithm to a first set of two-dimensional grayscale images to generate a first set of transformed two-dimensional grayscale images;
- applying at least one feature algorithm to at least one two-dimensional grayscale image and to each transformed two-dimensional grayscale images of the first set of transformed two-dimensional grayscale images;
- generating, based on the applying of the at least one feature algorithm to at least one two-dimensional grayscale image and to each transformed two-dimensional grayscale images, a plurality of feature values comprising a feature vector;
- projecting the three-dimensional image into a two-dimensional color image;
- applying at least one color transformation algorithm to a first set of two-dimensional color images to generate a first set of color-transformed two-dimensional grayscale images;
- applying at least one feature algorithm to at least one two-dimensional color image and to each color-transformed two-dimensional grayscale images;
- generating, based on the applying of the at least one feature algorithm to the at least one two-dimensional color image and to each of the transformed two-dimensional color images, a plurality of color feature values comprising the feature vector;
- collecting patient information;
- generating, based on the collected patient information, one or more patient values comprising the feature vector;
- training a machine learning model based on the feature vector and an associated diagnosis of the patient tissue, the machine learning model comprising a classifier having a weighted value assigned to each of the plurality of feature values, the plurality of color feature values, and the patient value.
8 more elements
A non-transitory computer-readable storage medium including program code which when executed by at least one processor causes operations comprising:
- projecting a three-dimensional image of a patient tissue into a plurality of two-dimensional grayscale images;
- applying at least one transformation algorithm to a first set of two-dimensional grayscale images to generate a first set of transformed two-dimensional grayscale images;
- applying at least one feature algorithm to at least one two-dimensional grayscale image and to each transformed two-dimensional grayscale images of the first set of transformed two-dimensional grayscale images;
- generating, based on the applying of the at least one feature algorithm to at least one two-dimensional grayscale image and to each transformed two-dimensional grayscale images, a plurality of feature values comprising a feature vector;
- projecting the three-dimensional image into a two-dimensional color image;
- applying at least one color transformation algorithm to a first set of two-dimensional color images to generate a first set of color-transformed two-dimensional grayscale images;
- applying at least one feature algorithm to at least one two-dimensional color image and to each color-transformed two-dimensional grayscale images;
- generating, based on the applying of the at least one feature algorithm to the at least one two-dimensional color image and to each of the transformed two-dimensional color images, a plurality of color feature values comprising the feature vector;
- collecting patient information;
- generating, based on the collected patient information, one or more patient values comprising the feature vector;
- training a machine learning model based on the feature vector and an associated diagnosis of the patient tissue, the machine learning model comprising a classifier having a weighted value assigned to each of the plurality of feature values, the plurality of color feature values, and the patient value.
8 more elements
Dependencies are inferred from the claim text.