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Initial CDAS Request Approval
Oct 19, 2020
Detection of pulmonary nodules
Lung cancer screening using low-dose computed tomography has been shown to reduce mortality by 20-43% and is now included in US screening guidelines. Existing challenges include inter-grader variability and high false-positive and false-negative rates. We propose a deep learning algorithm that uses a patient's current and prior computed tomography volumes to predict the risk of lung cancer. While the vast majority of patients remain unscreened, we show the potential for deep learning models to increase the accuracy, consistency and adoption of lung cancer screening worldwide.
1.We propose a deep learning algorithm that uses a patient’s current and prior computed tomography volumes to predict the risk of lung cancer.
2.We used a 3D-CNN network to segment the lung area.
3.We used 3D-CNN network to detect pulmonary nodules
4.The confidence of the test results was predicted and the degree of malignancy was predicted
Qingdao University of Science and Technology