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1. WO2022112568 - CLASSIFYING IMAGES OF DOSE-RESPONSE GRAPHS

Publication Number WO/2022/112568
Publication Date 02.06.2022
International Application No. PCT/EP2021/083404
International Filing Date 29.11.2021
IPC
G16H 20/10 2018.1
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
20ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
10relating to drugs or medications, e.g. for ensuring correct administration to patients
G06N 3/04 2006.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architecture, e.g. interconnection topology
G06N 3/08 2006.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
CPC
G06N 3/045
G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
G16H 20/10
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
20ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
10relating to drugs or medications, e.g. for ensuring correct administration to patients
Applicants
  • SANOFI [FR]/[FR]
Inventors
  • BIANCIOTTO, Marc
  • MI, Kun
Agents
  • DERRY, Paul
Priority Data
20315469.530.11.2020EP
Publication Language English (en)
Filing Language English (EN)
Designated States
Title
(EN) CLASSIFYING IMAGES OF DOSE-RESPONSE GRAPHS
(FR) CLASSIFICATION D'IMAGES DE GRAPHIQUES DOSE-RÉPONSE
Abstract
(EN) A computer-implemented method of classifying images comprising dose-response graphs obtained from dose-response experiments. The method comprises receiving, at a curve shape classifier model, an input comprising image data including a plurality of pixels, wherein the image data represents an image of a dose-response graph indicating a relationship between the concentration of a compound and its activity. The curve shape classifier model comprises a neural network model configured for classifying images of dose-response graphs into a plurality of dose-response graph categories relating to curve shape. The method further comprises generating, using the neural network model, a classification output for the image represented by the received image data, said generating comprising processing the image data using one or more layers of the neural network model in accordance with parameters associated with the one or more layers.
(FR) L'invention concerne un procédé mis en œuvre par ordinateur de classification d'images comprenant des graphiques dose-réponse obtenus à partir d'expériences de dose-réponse. Le procédé comprend la réception, au niveau d'un modèle de classificateur de forme de courbe, d'une entrée comprenant des données d'image contenant une pluralité de pixels, les données d'image représentant une image d'un graphique dose-réponse indiquant une relation entre la concentration d'un composé et son activité. Le modèle de classificateur de forme de courbe comprend un modèle de réseau neuronal configuré pour classifier des images de graphiques dose-réponse en une pluralité de catégories de graphiques dose-réponse se rapportant à une forme de courbe. Le procédé comprend en outre la génération, en utilisant le modèle de réseau neuronal, d'une sortie de classification pour l'image représentée par les données d'image reçues, ladite génération comprenant le traitement des données d'image en utilisant une ou plusieurs couches du modèle de réseau neuronal en fonction de paramètres associés auxdites couches.
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