HISTOPATHOLOGICAL IMAGE ANALYSIS

An apparatus and computer-implemented method for training a machine-learning algorithm to perform histopathological analysis is disclosed. The method comprises obtaining (210) a plurality of first microscopic images of first histological specimens that have been stained with a first marker; and obta...

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Hauptverfasser:	Maddison, John Robert, Danielsen, Havard
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HANDLING RECORD CARRIERS IMAGE DATA PROCESSING OR GENERATION, IN GENERAL INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES MEASURING PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS TESTING
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creator	Maddison, John Robert Danielsen, Havard
description	An apparatus and computer-implemented method for training a machine-learning algorithm to perform histopathological analysis is disclosed. The method comprises obtaining (210) a plurality of first microscopic images of first histological specimens that have been stained with a first marker; and obtaining (212), a respective plurality of second microscopic images of second histological specimens that have been stained with a second, different marker. The method further comprises obtaining (220) a respective plurality of mask images generated for the second microscopic images, each mask image identifying a histological feature of interest highlighted in the respective second microscopic image by the second marker. The method comprises training (240) the machine-learning algorithm to predict, from a first microscopic image, a histological feature of interest that would be highlighted in the same specimen by the second marker. Also disclosed is an apparatus and computer-implemented method for histopathological analysis using the trained machine-learning algorithm.
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The method comprises obtaining (210) a plurality of first microscopic images of first histological specimens that have been stained with a first marker; and obtaining (212), a respective plurality of second microscopic images of second histological specimens that have been stained with a second, different marker. The method further comprises obtaining (220) a respective plurality of mask images generated for the second microscopic images, each mask image identifying a histological feature of interest highlighted in the respective second microscopic image by the second marker. The method comprises training (240) the machine-learning algorithm to predict, from a first microscopic image, a histological feature of interest that would be highlighted in the same specimen by the second marker. 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The method comprises obtaining (210) a plurality of first microscopic images of first histological specimens that have been stained with a first marker; and obtaining (212), a respective plurality of second microscopic images of second histological specimens that have been stained with a second, different marker. The method further comprises obtaining (220) a respective plurality of mask images generated for the second microscopic images, each mask image identifying a histological feature of interest highlighted in the respective second microscopic image by the second marker. The method comprises training (240) the machine-learning algorithm to predict, from a first microscopic image, a histological feature of interest that would be highlighted in the same specimen by the second marker. 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The method comprises obtaining (210) a plurality of first microscopic images of first histological specimens that have been stained with a first marker; and obtaining (212), a respective plurality of second microscopic images of second histological specimens that have been stained with a second, different marker. The method further comprises obtaining (220) a respective plurality of mask images generated for the second microscopic images, each mask image identifying a histological feature of interest highlighted in the respective second microscopic image by the second marker. The method comprises training (240) the machine-learning algorithm to predict, from a first microscopic image, a histological feature of interest that would be highlighted in the same specimen by the second marker. Also disclosed is an apparatus and computer-implemented method for histopathological analysis using the trained machine-learning algorithm.</abstract><oa>free_for_read</oa></addata></record>
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subjects	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HANDLING RECORD CARRIERS IMAGE DATA PROCESSING OR GENERATION, IN GENERAL INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES MEASURING PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS TESTING
title	HISTOPATHOLOGICAL IMAGE ANALYSIS
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