Inc.;University of Central Florida Research Foundation
发明人:
Ulas Bagci,Naji Khosravan,Sarfaraz Hussein
申请号：
US16673397
公开号：
US20200160997A1
申请日:
2019.11.04
申请国别(地区):
US
年份:
2020
代理人:
摘要：
A method of detecting and diagnosing cancers characterized by the presence of at least one nodule/neoplasm from an imaging scan is presented. To detect nodules in an imaging scan, a 3D CNN using a single feed forward pass of a single network is used. After detection, risk stratification is performed using a supervised or an unsupervised deep learning method to assist in characterizing the detected nodule/neoplasm as benign or malignant. The supervised learning method relies on a 3D CNN used with transfer learning and a graph regularized sparse MTL to determine malignancy. The unsupervised learning method uses clustering to generate labels after which label proportions are used with a novel algorithm to classify malignancy. The method assists radiologists in improving detection rates of lung nodules to facilitate early detection and minimizing errors in diagnosis.
