US 2022/0020151 A1 · EVALUATING A MAMMOGRAM USING A PLURALITY OF PRIOR MAMMOGRAMS AND DEEP LEARNING ALGORITHMS

Abstract

An approach for training, on a computer, one or more deep learning algorithms with a plurality of mammograms with known outcomes based, at least in part, on using a set of mammograms of each patient in the plurality of mammograms. The approach includes receiving a first set of mammograms of a first patient. The first set of mammograms includes an unevaluated mammogram of the first patient and a set of prior mammograms of the first patient. The approach includes the trained convolutional neural network extracting the set of features from each mammogram of the set of mammograms of the first patient. Furthermore, the approach includes using a second deep learning algorithm of the one or more deep learning algorithms to perform an evaluation of the unevaluated mammogram of the first patient based, at least in part, on the set of prior mammograms of the first patient.

Claims25

Dependent claims

A computer-implemented method, the computer-implemented method comprising:

training, by one or more computer processors, one or more deep learning algorithms based, at least in part, on using a set of mammograms of each patient of a plurality of patients from a plurality of mammograms with known outcomes;
receiving, by one or more computer processors, a first set of mammograms for a first patient wherein, the first set of mammograms includes an unevaluated mammogram of the first patient and a set of prior mammograms of the first patient;
extracting, by one or more computer processors, by a trained convolutional neural network, a set of features from each mammogram in the first set of mammograms of the first patient;
and
performing, by one or more computer processors, an evaluation of the unevaluated mammogram of the first patient using the trained convolutional neural network and a second trained deep learning algorithm based, at least in part, on the set of prior mammograms of the first patient.

2 more elements

A computer-implemented method, the computer-implemented method comprising:

extracting, by one or more computer processors, by a trained convolutional neural network, a set of features from each mammogram of a plurality of mammograms with known outcomes;
training, by one or more computer processors, a modified long-short term memory algorithm based, at least in part, on a set of mammograms of each patient in the plurality of mammograms with the known outcomes;
receiving, by one or more computer processors, a first set of mammograms of a first patient wherein, the first set of mammograms includes an unevaluated mammogram of the first patient and a set of prior mammograms of the first patient;
extracting, by one or more computer processors, by the trained convolutional neural network, the set of features from each mammogram of the first set of mammograms of the first patient;
and
performing, by one or more computer processors, an evaluation of the unevaluated mammogram of the first patient using the trained convolutional neural network and the trained modified long-short term memory algorithm based, at least in part, on the first set of mammograms of the patient.

3 more elements

A computer-implemented method, the computer-implemented method comprising:

extracting, by one or more computer processors, by a trained convolutional neural network, a set of features from each mammogram of a plurality of mammograms with known outcomes;
training, by one or more computer processors, a random forest classifier with the plurality of mammograms with the known outcomes based, at least in part, on using a set of mammograms of each patient of a plurality of patients in the plurality of mammograms with the known outcomes;
receiving, by one or more computer processors, a first set of mammograms of a first patient wherein, the first set of mammograms includes an unevaluated mammogram of the first patient and a set of prior mammograms of the first patient;
extracting, by one or more computer processors, by the trained convolutional neural network, the set of features from each mammogram of the first set of mammograms of the first patient;
and
performing, by one or more computer processors, an evaluation of the unevaluated mammogram of the first patient using a trained random forest classifier based, at least in part, on the set of prior mammograms of the first patient.

3 more elements

A computer program product comprising:

one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions executable by a processor, the program instructions comprising instructions for:
extracting, by one or more computer processors, by a trained convolutional neural network, a set of features from each mammogram of a plurality of mammograms with known outcomes;
training, by one or more computer processors, one or more deep learning algorithms based, at least in part, on using a set of mammograms of each patient of a plurality of patients from the plurality of mammograms with the known outcomes;
receiving, by one or more computer processors, a first set of mammograms for a first patient wherein, the first set of mammograms of the first patient includes an unevaluated mammogram of the first patient and a set of prior mammograms of the first patient;
extracting, by one or more computer processors, by the trained convolutional neural network, the set of features from each mammogram of the first set of mammograms of the first patient;
and
performing, by one or more computer processors, an evaluation of the unevaluated mammogram of the first patient using the trained convolutional neural network and a deep learning algorithm of the one or more trained deep learning algorithms based, at least in part, on the set of prior mammograms of the first patient.

4 more elements

A computer system comprising:

one or more computer processors;
one or more computer readable storage media;
program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising instructions to perform:
extracting, by one or more computer processors, by a trained convolutional neural network, a set of features from each mammogram of a plurality of mammograms with known outcomes;
training, by one or more computer processors, one or more deep learning algorithms based, at least in part, on using a set of mammograms of each patient of a plurality of patients from the plurality of mammograms with the known outcomes;
receiving, by one or more computer processors, a first set of mammograms for a first patient wherein, the first set of mammograms includes an unevaluated mammogram of the first patient and a set of prior mammograms of the first patient;
extracting, by one or more computer processors, by the trained convolutional neural network, the set of features from each mammogram of the first set of mammograms of the first patient;
and
performing, by one or more computer processors, an evaluation of the unevaluated mammogram of the first patient using the trained convolutional neural network and a trained deep learning algorithm of the one or more trained deep learning algorithms based, at least in part, on the set of prior mammograms of the first patient.

6 more elements

Dependencies are inferred from the claim text.

Evaluating a mammogram using a plurality of prior mammograms and deep learning algorithms

Life of this patent

Evaluating a mammogram using a plurality of prior mammograms and deep learning algorithms

Life of this patent

Abstract

Claims25