US 12,387,477 B2
Conjoined twin network for treatment and analysis
Sheida Nabavi, Wellesley, MA (US); Clifford Yang, Farmington, CT (US); and Jun Bai, Cincinnati, OH (US)
Assigned to UNIVERSITY OF CONNECTICUT, Farmington, CT (US)
Filed by University of Connecticut, Farmington, CT (US)
Filed on Aug. 30, 2024, as Appl. No. 18/821,269.
Application 18/821,269 is a continuation in part of application No. 18/096,700, filed on Jan. 13, 2023.
Claims priority of provisional application 63/299,313, filed on Jan. 13, 2022.
Prior Publication US 2024/0428577 A1, Dec. 26, 2024
Int. Cl. G06V 10/82 (2022.01); G06N 3/0455 (2023.01); G06N 3/048 (2023.01); G06T 7/00 (2017.01); G16H 20/00 (2018.01)
CPC G06V 10/82 (2022.01) [G06N 3/0455 (2023.01); G06N 3/048 (2023.01); G06T 7/0012 (2013.01); G16H 20/00 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06V 2201/032 (2022.01)] 21 Claims
OG exemplary drawing
 
1. An apparatus for treating an abnormality, the apparatus comprising:
a processor;
a non-transitory computer readable medium comprising:
a first convolutional neural network (CNN) having weights;
a second convolutional neural network in parallel with the first CNN and sharing the weights of the first CNN, the second CNN being joined to the first neural network by a distance function;
instructions that when executed by the processor implement a method comprising:
receiving a first image dataset of an area of interest and processing the first image dataset using the first CNN;
receiving a second image dataset of the area of interest obtained prior to the first image dataset and processing the second image dataset using the second CNN;
identifying the abnormality based on an output of the distance function; and
outputting an indication of the abnormality, wherein the indication influences administering or adjusting treatment of the abnormality;
wherein the first CNN comprises a first series of neural network layers that provide a current feature vector fC and the second CNN comprises a second series of neural network layers that provide a prior feature vector fP, fC and fP being input to a first distance function that provides output d1 and a second distance function that provides output d2, d1 and d2 being input to a sigmoid function that identifies the abnormality.