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 |
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.
|