US 11,056,220 B2
Utilizing density properties of anatomical features in an intensity transform augmentation system
Kevin Lyman, Fords, NJ (US); Li Yao, San Francisco, CA (US); Eric C. Poblenz, Palo Alto, CA (US); Jordan Prosky, San Francisco, CA (US); Ben Covington, Berkeley, CA (US); and Anthony Upton, Malvern (AU)
Assigned to Enlitic, Inc., San Francisco, CA (US)
Filed by Enlitic, Inc., San Francisco, CA (US)
Filed on Mar. 21, 2019, as Appl. No. 16/360,275.
Claims priority of provisional application 62/770,334, filed on Nov. 21, 2018.
Prior Publication US 2020/0160977 A1, May 21, 2020
Int. Cl. G16H 10/60 (2018.01); H04L 29/06 (2006.01); G16H 30/40 (2018.01); G16H 15/00 (2018.01); G06K 9/62 (2006.01); G06T 5/00 (2006.01); G06T 5/50 (2006.01); G06T 7/00 (2017.01); G06T 11/00 (2006.01); G06N 5/04 (2006.01); G16H 30/20 (2018.01); G06N 20/00 (2019.01); G06F 9/54 (2006.01); G06T 7/187 (2017.01); G06T 7/11 (2017.01); G06F 3/0482 (2013.01); G06T 3/40 (2006.01); A61B 5/00 (2006.01); G16H 50/20 (2018.01); G06F 21/62 (2013.01); G06Q 20/14 (2012.01); G16H 40/20 (2018.01); G06F 3/0484 (2013.01); G06Q 10/06 (2012.01); G16H 10/20 (2018.01); G06T 7/10 (2017.01); G06T 11/20 (2006.01); G06F 16/245 (2019.01); G06T 7/44 (2017.01); G06N 20/20 (2019.01); G06K 9/20 (2006.01); H04L 29/08 (2006.01); G16H 50/70 (2018.01); G06T 7/70 (2017.01); G16H 50/30 (2018.01); A61B 5/055 (2006.01); A61B 6/03 (2006.01); A61B 8/00 (2006.01); G06K 9/66 (2006.01); A61B 6/00 (2006.01); G06Q 50/22 (2018.01); G06F 40/295 (2020.01)
CPC G16H 10/60 (2018.01) [A61B 5/7264 (2013.01); G06F 3/0482 (2013.01); G06F 3/0484 (2013.01); G06F 9/542 (2013.01); G06F 16/245 (2019.01); G06F 21/6254 (2013.01); G06K 9/2063 (2013.01); G06K 9/6231 (2013.01); G06K 9/6254 (2013.01); G06K 9/6256 (2013.01); G06K 9/6262 (2013.01); G06K 9/6277 (2013.01); G06N 5/04 (2013.01); G06N 5/045 (2013.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06Q 10/06315 (2013.01); G06Q 20/14 (2013.01); G06T 3/40 (2013.01); G06T 5/002 (2013.01); G06T 5/008 (2013.01); G06T 5/50 (2013.01); G06T 7/0012 (2013.01); G06T 7/0014 (2013.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G06T 7/187 (2017.01); G06T 7/44 (2017.01); G06T 7/97 (2017.01); G06T 11/001 (2013.01); G06T 11/006 (2013.01); G06T 11/206 (2013.01); G16H 10/20 (2018.01); G16H 15/00 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 40/20 (2018.01); G16H 50/20 (2018.01); H04L 67/12 (2013.01); H04L 67/42 (2013.01); A61B 5/055 (2013.01); A61B 6/032 (2013.01); A61B 6/5217 (2013.01); A61B 8/4416 (2013.01); G06F 40/295 (2020.01); G06K 9/6229 (2013.01); G06K 9/6267 (2013.01); G06K 9/66 (2013.01); G06K 2209/05 (2013.01); G06Q 50/22 (2013.01); G06T 7/70 (2017.01); G06T 2200/24 (2013.01); G06T 2207/10048 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/10132 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01); G06T 2207/30008 (2013.01); G06T 2207/30016 (2013.01); G06T 2207/30061 (2013.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01)] 19 Claims
OG exemplary drawing
 
1. An intensity transformation augmentation system, comprising:
at least one processor; and
a memory that stores operational instructions that, when executed by the at least one processor, cause the intensity transformation augmentation system to:
receive, via a receiver, a training set of medical scans;
generate a plurality of sets of augmented images, wherein each set of augmented images in the plurality of sets of augmented images is generated by performing a set of intensity transformation functions on one of the training set of medical scans, and wherein each of the set of intensity transformation functions are based on density properties of corresponding one of a plurality of different anatomy features present in the training set of medical scans;
generate a computer vision model by performing a training step on the plurality of sets of augmented images, wherein each augmented image of a set of augmented images is assigned same output label data based on a corresponding one of the training set of medical scans, wherein the training set of medical scans includes a first subset of medical scans that corresponds to a first abnormality output label and a second subset of medical scans that corresponds to a second abnormality output label, wherein a first proper subset of the set of intensity transformation functions is applied to medical scans of the first subset based on first density properties of the first abnormality output label, wherein a second proper subset of the set of intensity transformation functions is applied to medical scans of the second subset based on second density properties of the second abnormality output label, and wherein a set difference between the first proper subset of the set of intensity transformation functions and the second proper subset of the set of intensity transformation functions is non-null;
receive, via the receiver, a new medical scan;
generate inference data by performing an inference function that utilizes the computer vision model on the new medical scan; and
transmit, via a transmitter, the inference data to a client device for display via a display device.