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US 11,699,233 B2Granted
Digital pathology using an artificial neural network
AssigneeSiemens Healthcare GmbH
InventorsMarvin Teichmann, Andre Aichert, Birgi Tamersoy, Martin Kraus, Arnaud Arindra Adiyoso, Tobias Heimann
Life of this patent
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Abstract
Various example embodiments pertain to processing images that depict tissue samples using a neural network algorithm. The neural network algorithm includes multiple encoder branches that are copies of each other that share the same parameters. The encoder branches can, accordingly, be referred to as Siamese copies of each other.
Claims17
A computer-implemented method, comprising:
- obtaining an input mage, the input image depicting a tissue sample;
- determining multiple tiles of the input image;
- processing each tile of the multiple tiles in a respective encoder branch of a neural network algorithm, to obtain a respective latent feature data structure;
- and
- using a decoder branch of the neural network algorithm, the using including,
- aggregating the latent feature data structures of the multiple tiles, to obtain a merged latent feature data structure, and
- processing the merged latent feature data structure to infer at least one semantic histopathology feature associated with the tissue sample,
- wherein the multiple encoder branches processing the multiple tiles share the same parameters.
5 more elements
A computer-implemented method, comprising:
- obtaining a training input image, the training input image depicting a tissue sample;
- obtaining a ground-truth label for at least one semantic histopathology feature associated with the training input image;
- determining multiple tiles of the training input image;
- and
- performing an end-to-end training of a neural network algorithm based on the ground-truth label, the neural network algorithm comprising multiple encoder branches and a decoder branch, each encoder branch processing a respective tile of the multiple tiles, the decoder branch processing a merged latent feature data structure obtained from aggregating latent feature data structures obtained from the multiple encoder branches,
- wherein the multiple encoder branches share the same parameters.
3 more elements
A device comprising:
- at least one processor configured to,
- obtain an input image, the input image depicting a tissue sample,
- determine multiple tiles of the input image,
- process each tile of the multiple tiles in a respective encoder branch of a neural network algorithm, to obtain a respective latent feature data structure, and
- use a decoder branch of the neural network algorithm to,
- aggregate the latent feature data structures of the multiple tiles to obtain a merged latent feature data structure, and
- process the merged latent feature data structure to infer at least one semantic histopathology feature associated with the tissue sample,
- wherein the multiple encoder branches share the same parameters.
5 more elements
A device comprising:
- at least one processor configured to,
- obtain a training input image, the training input image depicting a tissue sample,
- obtain a ground-truth label for at least one semantic histopathology feature associated with the training input image,
- determine multiple tiles of the training input image,
- perform an end-to-end training of a neural network algorithm based on the ground-truth label, the neural network algorithm comprising multiple encoder branches and a decoder branch, each encoder branch configured to process a respective tile of the multiple tiles, the decoder branch configured to process a merged latent feature data structure obtained from aggregating latent feature data structures obtained from the multiple encoder branches, wherein the multiple encoder branches share the same parameters.
2 more elements
Dependencies are inferred from the claim text.