US 12,106,549 B2
Self-supervised learning for artificial intelligence-based systems for medical imaging analysis
Florin-Cristian Ghesu, Baiersdori (DE); Bogdan Georgescu, Princeton, NJ (US); Awais Mansoor, Potomac, MD (US); Sasa Grbic, Plainsboro, NJ (US); and Dorin Comaniciu, Princeton, NJ (US)
Assigned to Siemens Healthineers AG, Forchheim (DE)
Filed by Siemens Healthineers AG, Frochheim (DE)
Filed on Nov. 12, 2021, as Appl. No. 17/454,696.
Prior Publication US 2023/0154164 A1, May 18, 2023
Int. Cl. G06V 10/774 (2022.01); G06V 10/82 (2022.01); G16H 30/40 (2018.01)
CPC G06V 10/7747 (2022.01) [G06V 10/82 (2022.01); G16H 30/40 (2018.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
for each respective training medical image of a set of unannotated training medical images:
generating a first augmented image by applying a first augmentation operation to the respective training medical image,
generating a second augmented image by applying a second augmentation operation to the respective training medical image,
creating a first representation vector from the first augmented image using an encoder network,
creating a second representation vector from the second augmented image using the encoder network,
mapping the first representation vector to first cluster codes, and
mapping the second representation vector to second cluster codes; and
optimizing the encoder network by 1) calculating a first loss based on the first representation vectors and the second cluster codes and 2) calculating a second loss based on the second representation vectors and the first cluster codes.