Deep learning for lung cancer nodules detection and classification in CT scans

D Riquelme, MA Akhloufi - Ai, 2020 - mdpi.com
Detecting malignant lung nodules from computed tomography (CT) scans is a hard and time-
consuming task for radiologists. To alleviate this burden, computer-aided diagnosis (CAD) …

Big data, big knowledge: big data for personalized healthcare

M Viceconti, P Hunter, R Hose - IEEE journal of biomedical and …, 2015 - ieeexplore.ieee.org
The idea that the purely phenomenological knowledge that we can extract by analyzing
large amounts of data can be useful in healthcare seems to contradict the desire of VPH …

Automated pulmonary nodule detection in CT images using deep convolutional neural networks

H **e, D Yang, N Sun, Z Chen, Y Zhang - Pattern recognition, 2019 - Elsevier
Lung cancer is one of the leading causes of cancer-related death worldwide. Early
diagnosis can effectively reduce the mortality, and computer-aided diagnosis (CAD) as an …

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge

AAA Setio, A Traverso, T De Bel, MSN Berens… - Medical image …, 2017 - Elsevier
Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has
been an active area of research for the last two decades. However, there have only been …

Abnormal lung quantification in chest CT images of COVID‐19 patients with deep learning and its application to severity prediction

F Shan, Y Gao, J Wang, W Shi, N Shi, M Han… - Medical …, 2021 - Wiley Online Library
Objective Computed tomography (CT) provides rich diagnosis and severity information of
COVID‐19 in clinical practice. However, there is no computerized tool to automatically …

Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks

AAA Setio, F Ciompi, G Litjens, P Gerke… - IEEE transactions on …, 2016 - ieeexplore.ieee.org
We propose a novel Computer-Aided Detection (CAD) system for pulmonary nodules using
multi-view convolutional networks (ConvNets), for which discriminative features are …

Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification

W Zhu, C Liu, W Fan, X **e - 2018 IEEE winter conference on …, 2018 - ieeexplore.ieee.org
In this work, we present a fully automated lung computed tomography (CT) cancer diagnosis
system, DeepLung. DeepLung consists of two components, nodule detection (identifying the …

Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection

Q Dou, H Chen, L Yu, J Qin… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
Objective: False positive reduction is one of the most crucial components in an automated
pulmonary nodule detection system, which plays an important role in lung cancer diagnosis …

Towards automatic pulmonary nodule management in lung cancer screening with deep learning

F Ciompi, K Chung, SJ Van Riel, AAA Setio… - Scientific reports, 2017 - nature.com
The introduction of lung cancer screening programs will produce an unprecedented amount
of chest CT scans in the near future, which radiologists will have to read in order to decide …

Lung nodule detection based on faster R-CNN framework

Y Su, D Li, X Chen - Computer Methods and Programs in Biomedicine, 2021 - Elsevier
Background Lung cancer is a worldwide high-risk disease, and lung nodules are the main
manifestation of early lung cancer. Automatic detection of lung nodules reduces the …