US 11,730,387 B2
Method for detection and diagnosis of lung and pancreatic cancers from imaging scans
Ulas Bagci, Orlando, FL (US); Naji Khosravan, Orlando, FL (US); and Sarfaraz Hussein, Orlando, FL (US)
Assigned to University of Central Florida Research Foundation, Inc., Orlando, FL (US)
Filed by University of Central Florida Research Foundation, Inc., Orlando, FL (US)
Filed on Nov. 4, 2019, as Appl. No. 16/673,397.
Claims priority of provisional application 62/755,018, filed on Nov. 2, 2018.
Prior Publication US 2020/0160997 A1, May 21, 2020
Int. Cl. A61B 5/00 (2006.01); A61B 5/055 (2006.01); A61B 6/00 (2006.01); A61B 6/03 (2006.01); A61B 6/12 (2006.01); A61B 8/08 (2006.01); G16H 50/20 (2018.01); G06N 3/04 (2023.01); G06N 20/10 (2019.01); G06N 5/04 (2023.01); G06N 3/08 (2023.01); G16H 30/40 (2018.01); G16H 70/60 (2018.01); G06T 7/00 (2017.01); G06F 18/23 (2023.01); G06F 18/214 (2023.01); G06F 18/2411 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01)
CPC A61B 5/055 (2013.01) [A61B 6/032 (2013.01); A61B 6/037 (2013.01); A61B 6/4417 (2013.01); A61B 6/5217 (2013.01); A61B 8/481 (2013.01); A61B 8/5223 (2013.01); G06F 18/2148 (2023.01); G06F 18/23 (2023.01); G06F 18/2411 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06N 5/04 (2013.01); G06N 20/10 (2019.01); G06T 7/0012 (2013.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 70/60 (2018.01); G06T 2207/10081 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30064 (2013.01); G06T 2207/30096 (2013.01); G06V 2201/032 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method of detecting and diagnosing cancer characterized by presence of at least one nodule in a subject comprising:
providing an imaging scan of the subject;
automatically detecting the presence of the at least one nodule in the imaging scan using a volumetric method of the whole imaging scan, the volumetric method using a 3D convolutional neural network (CNN) having convolution blocks with dense connections wherein a cell-wise classification of input is done in a single feed forward path of the CNN in one shot to detect all of the at least one nodule in a given volume simultaneously and in the absence of postprocessing or other steps to remove false positives; and
automatically determining a classification of malignancy of the at least one detected nodule in the imaging scan using a supervised or an unsupervised deep learning method;
wherein the supervised learning method comprising
automatically determining imaging attributes of the at least one nodule using transfer learning of a pre-trained 3D convolutional neural network (C3D);
fine-tuning the C3D network with binary labels for malignancy and the imaging attributes; and
incorporating the binary labels for malignancy and the binary labels for the imaging attributes of the at least one nodule into a graph regularized sparse multi-task learning (MTL) framework to obtain the classification of malignancy of the at least one nodule;
wherein the unsupervised learning method comprising
performing clustering on the imaging attributes of the at least one nodule to estimate an initial set of labels;
computing label proportions corresponding to each cluster; and
training a classifier using the label proportions and clusters to obtain the classification of malignancy of the at least one nodule.