US 11,699,233 B2
Digital pathology using an artificial neural network
Marvin Teichmann, Erlangen (DE); Andre Aichert, Erlangen (DE); Birgi Tamersoy, Erlangen (DE); Martin Kraus, Fuerth (DE); Arnaud Arindra Adiyoso, Nuremberg (DE); and Tobias Heimann, Erlangen (DE)
Assigned to SIEMENS HEALTHCARE GMBH, Erlangen (DE)
Filed by Siemens Healthcare GmbH, Erlangen (DE)
Filed on Mar. 28, 2022, as Appl. No. 17/705,887.
Claims priority of application No. 10 2021 203 251.6 (DE), filed on Mar. 31, 2021.
Prior Publication US 2022/0319000 A1, Oct. 6, 2022
Int. Cl. G06T 7/00 (2017.01); G06V 10/82 (2022.01); G06T 7/11 (2017.01); G06V 10/774 (2022.01); G06V 20/69 (2022.01)
CPC G06T 7/0012 (2013.01) [G06T 7/11 (2017.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06T 2207/10056 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01); G06V 20/698 (2022.01)] 17 Claims
OG exemplary drawing
 
1. 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.