US 12,430,767 B2
Systems, methods, and apparatuses for implementing contrastive learning via reconstruction within a self-supervised learning framework
Ruibin Feng, Scottsdale, AZ (US); Zongwei Zhou, Tempe, AZ (US); and Jianming Liang, Scottsdale, AZ (US)
Assigned to Arizona Board of Regents on behalf of Arizona State University, Scottsdale, AZ (US)
Filed by Arizona Board of Regents on behalf of Arizona State University, Scottsdale, AZ (US)
Filed on Oct. 8, 2021, as Appl. No. 17/497,528.
Claims priority of provisional application 63/222,331, filed on Jul. 15, 2021.
Claims priority of provisional application 63/089,455, filed on Oct. 8, 2020.
Prior Publication US 2022/0114733 A1, Apr. 14, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/11 (2017.01); G06N 3/088 (2023.01); G06T 7/00 (2017.01)
CPC G06T 7/11 (2017.01) [G06N 3/088 (2013.01); G06T 7/0012 (2013.01); G06T 2200/04 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20132 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
a memory to store instructions;
a processor to execute the instructions stored in the memory;
wherein the system is specially configured to execute the instructions via the processor to:
receive at the system as input a plurality of whole three-dimensional (3D) medical images;
for each of the plurality of whole 3D medical images:
randomly crop each of a plurality of 3D parts from the whole 3D medical image received at the system as input;
resize each of the plurality of randomly cropped 3D parts;
predict, based on each of the randomly cropped and resized plurality of 3D parts, the whole 3D medical image;
reconstruct the whole 3D medical image from the predicted whole 3D medical image;
measure a reconstruction loss, according to a reconstruction loss function, in the reconstructed whole 3D medical image compared to the whole 3D medical image; and
analyze the reconstruction loss against the whole 3D medical image representing a known ground truth image to the reconstruction loss function.