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1. (WO2018165103) MACHINE LEARNING FOR DIGITAL PATHOLOGY
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Pub. No.: WO/2018/165103 International Application No.: PCT/US2018/021060
Publication Date: 13.09.2018 International Filing Date: 06.03.2018
IPC:
G16H 50/20 (2018.01) ,G16H 50/30 (2018.01) ,G06T 7/00 (2006.01) ,G01N 33/574 (2006.01)
[IPC code unknown for G16H 50/20][IPC code unknown for G16H 50/30]
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
T
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7
Image analysis, e.g. from bit-mapped to non bit-mapped
G PHYSICS
01
MEASURING; TESTING
N
INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
33
Investigating or analysing materials by specific methods not covered by groups G01N1/-G01N31/131
48
Biological material, e.g. blood, urine; Haemocytometers
50
Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
53
Immunoassay; Biospecific binding assay; Materials therefor
574
for cancer
Applicants:
UNIVERSITY OF SOUTHERN CALIFORNIA [US/US]; 1150 South Olive Street Suite 2300 Los Angeles, California 90015, US
Inventors:
AGUS, David B.; US
MACKLIN, Paul Thomas; US
RAWAT, Rishi Raghav; US
RUDERMAN, Daniel Lee; US
Agent:
PROSCIA, James W.; US
KUSHMAN, James A.; US
NEMAZI, John E.; US
BRODBINE, Michael S.; US
Priority Data:
62/467,57906.03.2017US
Title (EN) MACHINE LEARNING FOR DIGITAL PATHOLOGY
(FR) APPRENTISSAGE MACHINE POUR PATHOLOGIE NUMÉRIQUE
Abstract:
(EN) A method assessing tissue morphology using machine learning includes a step of training a machine learnable device to predict the status of a diagnostic feature in stained tissue samples. The machine learnable device is trained with a characterized set of digital images of stained tissue samples. Each digital image of the characterized set has a known status for the diagnostic feature and an extracted feature map provides values for a extracted feature over an associated 2-dimensional grid of spatial locations. A step of inputting the set of extracted feature maps is inputted into the machine learnable device to form associations therein between the set of extracted feature maps to and the known status for the diagnostic feature to form a trained machine learnable device. The status for the diagnostic feature of a stained tissue sample of unknown status for the diagnostic feature is predicted from the trained machine learnable device.
(FR) L'invention concerne un procédé évaluant la morphologie d'un tissu au moyen d'un apprentissage machine comprenant une étape permettant d'entraîner un dispositif pouvant apprendre par apprentissage machine afin de prédire l'état d'un élément caractéristique dans des échantillons de tissus contaminés. Le dispositif pouvant apprendre par apprentissage machine est entraîné au moyen d'un ensemble caractérisé d'images numériques d'échantillons de tissus contaminés. Chaque image numérique de l'ensemble caractérisé possède un état connu de l'élément caractéristique et une signature extraite fournit des valeurs destinées à une signature extraite sur une grille bidimensionnelle associée à des localisations spatiales. Une étape consiste à entrer l'ensemble de signatures extraites dans le dispositif pouvant apprendre par apprentissage machine pour y former des associations entre l'ensemble de signatures extraites et l'état connu des éléments caractéristiques afin de former un dispositif entraîné par apprentissage machine. L'état de l'élément caractéristique d'un échantillon de tissu contaminé d'état inconnu est prédit par le dispositif entraîné par apprentissage machine.
front page image
Designated States: AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW
African Regional Intellectual Property Organization (ARIPO) (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, UG, ZM, ZW)
Eurasian Patent Office (AM, AZ, BY, KG, KZ, RU, TJ, TM)
European Patent Office (EPO) (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TR)
African Intellectual Property Organization (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, KM, ML, MR, NE, SN, TD, TG)
Publication Language: English (EN)
Filing Language: English (EN)