Attention-based multiple instance learning for whole slide images
AssigneeGenentech, Inc.
InventorFang-Yao HU
Life of this patent
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Abstract
In one embodiment, a method includes, receiving a whole slide image and segmenting the whole slide image into multiple image tiles. The method includes generating a feature vector corresponding to each tile of the plurality of tiles, wherein the feature vector for each of the tiles represents an embedding for the tile. The method includes computing a weighting value corresponding to each embedding feature vector using an attention network. The method includes computing an image embedding based on the embedding feature vectors, wherein each embedding feature vector is weighted based on the weighting value corresponding to the embedding feature vector. The method includes generating a classification for the whole slide image based on the image embedding.
Claims20
A computer-implemented method comprising:
- receiving a whole slide image;
- segmenting the whole slide image into a plurality of tiles;
- generating a feature vector for each of the tiles, wherein the feature vector for each of the tiles represents an embedding for the tile;
- computing a weighting value corresponding to each of the feature vectors using an attention network;
- computing an image embedding based on the feature vectors, wherein each of the feature vectors is weighted based on the weighting value corresponding to the feature vector;
- and
- generating a classification for the whole slide image based on the image embedding.
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 whole slide image;
- segmenting the whole slide image into a plurality of tiles;
- generating a feature vector for each of the tiles, wherein the feature vector for each of the tiles represents an embedding for the tile;
- computing a weighting value corresponding to each of the feature vectors using an attention network;
- computing an image embedding based on the feature vectors, wherein each of the feature vectors is weighted based on the weighting value corresponding to the feature vector;
- and
- generating a classification for the whole slide image based on the image embedding.
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 whole slide image;
- segmenting the whole slide image into a plurality of tiles;
- generating a feature vector for each of the tiles, wherein the feature vector for each of the tiles represents an embedding for the tile;
- computing a weighting value corresponding to each of the feature vectors using an attention network;
- computing an image embedding based on the feature vectors, wherein each of the feature vectors is weighted based on the weighting value corresponding to the feature vector;
- and
- generating a classification for the whole slide image based on the image embedding.
Dependencies are inferred from the claim text.