US 2019/0318822 A1 · DEEP IMAGE CLASSIFICATION OF MEDICAL IMAGES

Abstract

Methods, systems, and storage components for utilizing a deep neural network(s) for classifying at least one medical image. A deep neural network (DNN) can be configured by an image processing component to contain at least one revived neuron, where the revived neuron has an adjusted value based on a focus-learning function that is transferred to the DNN by the image processing component, where the focus-learning function provides the adjustment by updating the DNN with data that contains a corrected classification with respect to at least one normal image sample derived from a medical image, and where the correction is based on the focus-learning function comparing an annotation associated with an abnormal image sample derived from the medical image to another annotation associated with the at least one normal image.

Claims25

Dependent claims

A computer implemented method for improving classification of a medical image comprising:

extracting, by a processor, a plurality of samples from the medical image, wherein the plurality of samples includes:
i) at least one image sample with a normal portion of organic matter and ii) at least one image sample with an abnormal portion of organic matter;
and
training a focus-learning function for an image analysis component with the plurality of samples by:
comparing a first classification made by the image analysis component as to at least one of:
i) the at least one normal image sample, and ii) the at least one abnormal image sample, to at least one annotation associated with at least one of:
i) the at least one normal image sample, and ii) the at least one abnormal image sample;
determining that the classification was erroneous based on the annotation;
and
updating the focus-learning function to reflect a correct classification of at least one of the i) the normal image sample and ii) the abnormal image sample based on the determination that the classification was erroneous.

7 more elements

A non-transitory computer-readable storage medium containing computer program code which, when executed by operation of one or more computer processors, performs an operation comprising:

extracting a plurality of samples from the at least one medical image, wherein the plurality of samples includes i) at least one image sample with a normal portion of organic matter and ii) at least one image sample with an abnormal portion of organic matter;
training a focus-learning function for an image analysis component with the plurality of samples by
comparing a first classification made by the image analysis component as to at least one of i) the at least one normal image sample and ii) the at least one abnormal image sample to at least one annotation associated with at least one of i) the at least one normal image sample and ii) the at least one abnormal image sample;
determining that the classification was erroneous based on the annotation;
and
updating the focus-learning function to reflect a correct classification of at least one of the i) the normal image sample and ii) the abnormal image sample based on the determination that the classification was erroneous.

3 more elements

A system comprising:

one or more computer processors;
and
a memory containing computer program code that, when executed by operation of the one or more computer processors, performs an operation comprising:
extracting a plurality of samples from the at least one medical image, wherein the plurality of samples includes i) at least one image sample with a normal portion of organic matter and ii) at least one image sample with an abnormal portion of organic matter;
training a focus-learning function for an image analysis component with the plurality of samples by:
comparing a first classification made by the image analysis component as to at least one of i) the at least one normal image sample and ii) the at least one abnormal image sample to at least one annotation associated with at least one of i) the at least one normal image sample and ii) the at least one abnormal image sample;
determining that the classification was erroneous based on the annotation;
and
updating the focus-learning function to reflect a correct classification of at least one of the i) the normal image sample and ii) the abnormal image sample based on the determination that the classification was erroneous.

6 more elements

Dependencies are inferred from the claim text.

Deep image classification of medical images

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Deep image classification of medical images

Life of this patent

Abstract

Claims25