US 12,293,295 B2
Histological image analysis
Sepp De Raedt, Oslo (NO); Ole-Johan Skrede, Oslo (NO); Håvard Emil Greger Danielsen, Oslo (NO); Tarjei Sveinsgjerd Hveem, Oslo (NO); Andreas Kleppe, Oslo (NO); and Knut Liestøl, Oslo (NO)
Assigned to OSLO UNIVERSITETSSYKEHUS, (NO)
Appl. No. 17/753,931
Filed by OSLO UNIVERSITETSSYKEHUS, Oslo (NO)
PCT Filed Sep. 18, 2020, PCT No. PCT/EP2020/076090
§ 371(c)(1), (2) Date Mar. 18, 2022,
PCT Pub. No. WO2021/053135, PCT Pub. Date Mar. 25, 2021.
Claims priority of application No. 1913616 (GB), filed on Sep. 20, 2019.
Prior Publication US 2024/0037747 A1, Feb. 1, 2024
Int. Cl. G06N 3/084 (2023.01); A61B 5/00 (2006.01); A61B 34/10 (2016.01); G06T 7/00 (2017.01); G06V 10/26 (2022.01); G06V 10/44 (2022.01); G06V 10/70 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 20/69 (2022.01); G06V 20/70 (2022.01); G06V 30/24 (2022.01); G16H 50/20 (2018.01)
CPC G06N 3/084 (2013.01) [A61B 5/4848 (2013.01); A61B 34/10 (2016.02); G06T 7/0016 (2013.01); G06V 10/26 (2022.01); G06V 10/454 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06V 10/87 (2022.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01); G06V 20/70 (2022.01); G06V 30/2504 (2022.01); G16H 50/20 (2018.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01); G06V 2201/03 (2022.01)] 23 Claims
OG exemplary drawing
 
1. A computer implemented system for determining an overall-classifier of one or more source-histopathological-images, wherein each source-histopathological-image has been obtained from one or more histopathological samples obtained from one or more subjects, and wherein each subject has been diagnosed as having, is suspected of having, is being treated for, has previously been treated for, and/or has previously had, cancer, the system comprising:
a first tile generator configured to generate a plurality of first-tiles from the one or more source-histopathological-images, wherein each of the plurality of first-tiles comprises a plurality of pixels that represents a region of the one or more source-histopathological-images having a first-area and a first-resolution;
a second tile generator configured to generate a plurality of second-tiles from the one or more source-histopathological-images, wherein each of the plurality of second-tiles comprises a plurality of pixels that represents a region of the one or more source-histopathological-images having a second-area and a second-resolution, wherein:
the first-area of the first-tiles is larger than the second-area of the second-tiles; and
the second-resolution of the second-tiles is higher than the first-resolution of the first-tiles;
a first machine-learning network configured to process the plurality of first-tiles in order to determine a first-classifier for the one or more source-histopathological-images, wherein the first machine-learning network (311) comprises:
a first-neural-network configured to process the plurality of first-tiles in order to determine a tile-feature for each of the plurality of first-tiles;
a pooling-function configured to combine subsets of the tile-features to generate a bag-feature for each of the subsets; and
a second-neural-network configured to process the bag-features in order to determine a first-classifier for the one or more source-histopathological-images, wherein the second-neural-network is a classification network;
a second machine-learning network configured to process the plurality of second-tiles in order to determine a second-classifier for the one or more source-histopathological-images, wherein the second machine-learning network comprises:
a first neural-network configured to process the plurality of second-tiles in order to determine a tile-feature for each of the plurality of second-tiles;
a pooling-function configured to combine subsets of the tile-features to generate a bag-feature for each of the subsets; and
a second-neural-network configured to process the bag-features in order to determine a second-classifier for the one or more source-histopathological-images, wherein the second-neural-network is a classification network; and
a classifier combiner configured to combine the first-classifier and the second-classifier to determine the overall-classifier for the one or more source-histopathological-images.