Abstract: A method for training a computer-implemented machine learning model for detecting irregularities in medical images, the method including: identifying at least one predetermined type of body region (14) depicted in a medical image (10), said body region (14) having a depicted irregularity (12); defining a plurality of image segments (20) each including at least part of the depicted body region (14), wherein a resolution of the image segments (20) is maintained or not reduced by more than 20% compared to the medical image (10); and using said image segments (20) to train a machine learning model to detect similar irregularities (12) in other medical images (10). Further, the invention relates to a use and to systems for training a computer-implemented machine learning model for detecting irregularities in medical images.
Type:
Grant
Filed:
November 8, 2019
Date of Patent:
June 15, 2021
Assignee:
OXIPIT, UAB
Inventors:
Jogundas Armaitis, Darius Baru{hacek over (s)}auskas, Jonas Bialopetravi{hacek over (c)}ius, Gediminas Pek{hacek over (s)}ys, Naglis Ramanauskas
Abstract: A method for training a computer-implemented machine learning model for detecting irregularities in medical images, the method including: identifying at least one predetermined type of body region (14) depicted in a medical image (10), said body region (14) having a depicted irregularity (12); defining a plurality of image segments (20) each including at least part of the depicted body region (14), wherein a resolution of the image segments (20) is maintained or not reduced by more than 20% compared to the medical image (10); and using said image segments (20) to train a machine learning model to detect similar irregularities (12) in other medical images (10). Further, the invention relates to a use and to systems for training a computer-implemented machine learning model for detecting irregularities in medical images.
Type:
Application
Filed:
November 8, 2019
Publication date:
May 14, 2020
Applicant:
OXIPIT, UAB
Inventors:
Jogundas ARMAITIS, Darius BARU{hacek over (S)}AUSKAS, Jonas BIALOPETRAVICIUS, Gediminas PEK{hacek over (S)}YS, Naglis RAMANAUSKAS