US 12,001,607 B2
System and method for iterative classification using neurophysiological signals
Amir B. Geva, Herzliya (IL); Eitan Netzer, Kiryat-Tivon (IL); Ran El Manor, Savyon (IL); Sergey Vaisman, Ramat-Gan (IL); Leon Y. Deouell, Tel-Aviv (IL); and Uri Antman, Ramat-HaSharon (IL)
Assigned to InnerEye Ltd., Herzeliya (IL)
Filed by InnerEye Ltd., Herzeliya (IL)
Filed on Feb. 8, 2023, as Appl. No. 18/107,037.
Application 18/107,037 is a continuation of application No. 16/471,587, granted, now 11,580,409, previously published as PCT/IB2017/058297, filed on Dec. 21, 2017.
Claims priority of provisional application 62/437,065, filed on Dec. 21, 2016.
Prior Publication US 2023/0185377 A1, Jun. 15, 2023
Int. Cl. G06F 3/01 (2006.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06F 18/23 (2023.01); G06F 18/2411 (2023.01); G06N 3/045 (2023.01); G06N 3/088 (2023.01); G06N 20/20 (2019.01); G06T 7/00 (2017.01); G06V 10/20 (2022.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01)
CPC G06F 3/015 (2013.01) [G06F 3/017 (2013.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06F 18/23 (2023.01); G06F 18/2411 (2023.01); G06N 3/045 (2023.01); G06N 3/088 (2013.01); G06N 20/20 (2019.01); G06T 7/0012 (2013.01); G06V 10/255 (2022.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 10/7788 (2022.01); G06V 10/82 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30016 (2013.01)] 21 Claims
OG exemplary drawing
 
21. A method of training an image classification neural network, the method comprising:
presenting a first plurality of images to an observer as a visual stimulus, while collecting neurophysiological signals from a brain of said observer;
processing said neurophysiological signals to identify a neurophysiological event indicative of a detection of a target by said observer in at least one image of said first plurality of images;
training the image classification neural network to identify the target in the image, based on said identification of said neurophysiological event; and
storing said trained image classification neural network in a non-transitory computer-readable storage medium;
wherein the method comprises applying unsupervised clustering to a second plurality of images, and selecting said first plurality of images from said second plurality of images based on said unsupervised clustering; and
wherein said image classification neural network comprises:
a first neural subnetwork configured for receiving and processing said neurophysiological data,
a second neural subnetwork configured for receiving and processing said second plurality of images,
a shared subnetwork having a neural network layer receiving and combining outputs from both said first neural subnetwork and said second neural subnetwork,
a first separate output layer for said first neural subnetwork outputting a first score, and
a second separate output layer for said second neural subnetwork outputting a second score;
and wherein the method comprises combining said first score with said second score as a weighted sum of said first and said second score, labeling said image with said weighted sum, and using said label in at least one iteration of said training.