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) …
consuming task for radiologists. To alleviate this burden, computer-aided diagnosis (CAD) …
[HTML][HTML] Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview
J Gao, Q Jiang, B Zhou, D Chen - Mathematical Biosciences and …, 2019 - aimspress.com
Computer-aided detection or diagnosis (CAD) has been a promising area of research over
the last two decades. Medical image analysis aims to provide a more efficient diagnostic and …
the last two decades. Medical image analysis aims to provide a more efficient diagnostic and …
Automated pulmonary nodule detection in CT images using deep convolutional neural networks
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 …
diagnosis can effectively reduce the mortality, and computer-aided diagnosis (CAD) as an …
Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification
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 …
system, DeepLung. DeepLung consists of two components, nodule detection (identifying the …
A 3D probabilistic deep learning system for detection and diagnosis of lung cancer using low-dose CT scans
O Ozdemir, RL Russell, AA Berlin - IEEE transactions on …, 2019 - ieeexplore.ieee.org
We introduce a new computer aided detection and diagnosis system for lung cancer
screening with low-dose CT scans that produces meaningful probability assessments. Our …
screening with low-dose CT scans that produces meaningful probability assessments. Our …
Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs
Y Gu, X Lu, L Yang, B Zhang, D Yu, Y Zhao… - Computers in biology …, 2018 - Elsevier
Objective A novel computer-aided detection (CAD) scheme for lung nodule detection using
a 3D deep convolutional neural network combined with a multi-scale prediction strategy is …
a 3D deep convolutional neural network combined with a multi-scale prediction strategy is …
Multi-task recurrent convolutional network with correlation loss for surgical video analysis
Surgical tool presence detection and surgical phase recognition are two fundamental yet
challenging tasks in surgical video analysis as well as very essential components in various …
challenging tasks in surgical video analysis as well as very essential components in various …
Semi-supervised adversarial model for benign–malignant lung nodule classification on chest CT
Classification of benign–malignant lung nodules on chest CT is the most critical step in the
early detection of lung cancer and prolongation of patient survival. Despite their success in …
early detection of lung cancer and prolongation of patient survival. Despite their success in …
Segmentation of lung nodules using improved 3D-UNet neural network
Lung cancer has one of the highest morbidity and mortality rates in the world. Lung nodules
are an early indicator of lung cancer. Therefore, accurate detection and image segmentation …
are an early indicator of lung cancer. Therefore, accurate detection and image segmentation …
3D deep learning from CT scans predicts tumor invasiveness of subcentimeter pulmonary adenocarcinomas
Identification of early-stage pulmonary adenocarcinomas before surgery, especially in cases
of subcentimeter cancers, would be clinically important and could provide guidance to …
of subcentimeter cancers, would be clinically important and could provide guidance to …