Assessing heterogeneity of features in digital pathology images using machine learning techniques
AssigneeGenentech, Inc.
InventorsCleopatra Kozlowski, Reheman Baikejiang
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Abstract
In one embodiment, a method includes, receiving a digital pathology image of a tissue sample and subdividing the digital pathology image into a plurality of patches. For each patch of the plurality of patches, the method includes identifying an image feature detected in the patch and generating one or more labels corresponding to the image feature identified in the patch using a machine-learning model. The method includes determining, based on the generated labels, a heterogeneity metric for the tissue sample. The method includes generating an assessment of the tissue sample based on the heterogeneity metric.
Claims19
A computer-implemented method comprising:
- receiving a digital pathology image of a tissue sample from a patient;
- subdividing the digital pathology image into a plurality of patches;
- for each patch of the plurality of patches:
- identifying a plurality of image features detected in the patch;
- and
- generating a plurality of labels corresponding to the plurality of image features identified in the patch using a trained machine-learning model, wherein the plurality of labels comprising one or more labels of a first label type and one or more labels of a second label type;
- determining, based on a relative frequency of the first label type relative to the second label type in the generated plurality of labels, a heterogeneity metric for the tissue sample, wherein the heterogeneity metric quantifies a degree of heterogeneity of the tissue sample;
- generating an assessment of the tissue sample based on the heterogeneity metric, wherein the assessment comprises a prediction of a responsiveness of the patient to one or more treatments;
- generating a patch-based signature based on the generated plurality of labels;
- and
- using the patch-based signature, generating a visualization of image features for evaluating the trained machine-learning model.
A digital pathology image processing system comprising:
- one or more processors;
- and
- one or more computer-readable non-transitory storage media coupled to one or more of the processors and comprising instructions operable when executed by one or more of the processors to cause the system to perform operations comprising:
- receiving a digital pathology image of a tissue sample from a patient;
- subdividing the digital pathology image into a plurality of patches;
- for each patch of the plurality of patches:
- identifying a plurality of image features detected in the patch;
- and
- generating a plurality of labels corresponding to the plurality of image features identified in the patch using a trained machine-learning model, wherein the plurality of labels comprising one or more labels of a first label type and one or more labels of a second label type;
- determining, based on a relative frequency of the first label type relative to the second label type in the generated plurality of labels, a heterogeneity metric for the tissue sample, wherein the heterogeneity metric quantifies a degree of heterogeneity of the tissue sample;
- generating an assessment of the tissue sample based on the heterogeneity metric, wherein the assessment comprises a prediction of a responsiveness of the patient to one or more treatments;
- generating a patch-based signature based on the generated plurality of labels;
- and
- using the patch-based signature, generating a visualization of image features for evaluating the machine-learning model.
One or more computer-readable non-transitory storage media including instructions that, when executed by one or more processors, are configured to cause the one or more processors of a digital pathology image processing system to perform operations comprising:
- receiving a digital pathology image of a tissue sample from a patient;
- subdividing the digital pathology image into a plurality of patches;
- for each patch of the plurality of patches:
- identifying a plurality of image features detected in the patch;
- and
- generating a plurality of labels corresponding to the plurality of image features identified in the patch using a trained machine-learning model, wherein the plurality of labels comprising one or more labels of a first label type and one or more labels of a second label type;
- determining, based on a relative frequency of the first label type relative to the second label type in the generated plurality of labels, a heterogeneity metric for the tissue sample, wherein the heterogeneity metric quantifies a degree of heterogeneity of the tissue sample;
- generating an assessment of the tissue sample based on the heterogeneity metric, wherein the assessment comprises a prediction of a responsiveness of the patient to one or more treatments;
- generating a patch-based signature based on the generated plurality of labels;
- and
- using the patch-based signature, generating a visualization of image features for evaluating the machine-learning model.
Dependencies are inferred from the claim text.