US 12,406,339 B2
Machine learning data augmentation using diffusion-based generative models
Eli Gibson, Plainsboro, NJ (US); and Boris Mailhe, Plainsboro, NJ (US)
Assigned to Siemens Healthineers AG, Forchheim (DE)
Filed by Siemens Healthineers AG, Forchheim (DE)
Filed on May 10, 2023, as Appl. No. 18/314,901.
Prior Publication US 2024/0378704 A1, Nov. 14, 2024
Int. Cl. G06T 5/70 (2024.01); G06T 5/10 (2006.01); G06T 11/00 (2006.01)
CPC G06T 5/70 (2024.01) [G06T 5/10 (2013.01); G06T 11/00 (2013.01); G06T 2207/20048 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20092 (2013.01); G06T 2207/30004 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving one or more input medical images;
applying at least one of noise and one or more transformations to the one or more input medical images to generate one or more noisy augmented images, wherein applying the at least one of the noise and the one or more transformations comprises generating an uncertainty map of uncertainty introduced by the at least one of the noise and the one or more transformations;
denoising the one or more noisy augmented images using a diffusion-based denoising system based on the uncertainty map to generate one or more denoised augmented images;
repeating the applying and the denoising for one or more iterations using the one or more denoised augmented images as the one or more input medical images to generate one or more final augmented images; and
outputting the one or more final augmented images.