Medical analytics system
First Claim
Patent Images
1. 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.
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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.
102 Citations
18 Claims
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1. A system, comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A computer-implemented method, comprising:
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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. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A non-transitory computer-readable storage medium including program code which when executed by at least one processor causes operations comprising:
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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. - View Dependent Claims (14, 15, 16, 17, 18)
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Specification