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Lymph node detection in MR Lymphography: false positive reduction using multi-view convolutional neural networks
2019
PeerJ
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Conclusion Multi-view convolutional neural networks significantly reduce false positives in a lymph node detection system for MRL images, and three orthogonal views are sufficient. ...
The CAD system was extended with three types of 2D multi-view convolutional neural networks (CNN) aiming to reduce false positives (FP). ...
False positive reduction method using multi-view CNNs The false positive reduction stage is an extension to the initial lymph node detection method described in the previous paragraph. ...
doi:10.7717/peerj.8052
pmid:31772836
pmcid:PMC6876485
fatcat:uuflvhq5xbe7vnqzufwghhev6u
Towards Single-phase Single-stage Detection of Pulmonary Nodules in Chest CT Imaging
[article]
2018
arXiv
pre-print
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Detection of pulmonary nodules in chest CT imaging plays a crucial role in early diagnosis of lung cancer. ...
Over the years, a range of systems have been proposed, mostly following a two-phase paradigm with: 1) candidate detection, 2) false positive reduction. ...
In this case, false negative detections, which are difficult to revise, are much more concerned than false positives, which are much easier. ...
arXiv:1807.05972v1
fatcat:offffmoh2bgorl6dgmpemelsjm
Multi-Scale Heterogeneous 3D CNN for False-Positive Reduction in Pulmonary Nodule Detection, Based on Chest CT Images
2019
Applied Sciences
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In this study, we propose a novel multi-scale heterogeneous three-dimensional (3D) convolutional neural network (MSH-CNN) based on chest computed tomography (CT) images. ...
Typically, a computer-aided diagnosis system for pulmonary nodule detection consists of two parts: candidate nodule extraction and false-positive reduction of candidate nodules. ...
Method Our proposed multi-scale heterogeneous 3D CNN framework for false-positive reduction in pulmonary nodule detection shown in Figure 2d consists of three main parts: 3D multi-scale gradual integration ...
doi:10.3390/app9163261
fatcat:ksliex2uore4vcggnf3a663gda
Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection
[article]
2019
arXiv
pre-print
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Using the thick MIP images helps the detection of small pulmonary nodules (3mm-10mm) and acquires fewer false positives. ...
projection based CNN framework for automatic pulmonary nodule detection in CT scans. ...
ACKNOWLEDGMENT The authors would like to thank Google for providing us with a research grant to run our computations on the Google Cloud Platform and NVIDIA for the support of the GPU. ...
arXiv:1904.05956v1
fatcat:7mllsz3x3zefrfyxjbt3zaes5i
Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection
2019
IEEE Transactions on Medical Imaging
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The use of thick MIP images helps the detection of small pulmonary nodules (3 mm-10 mm) and results in fewer false positives. ...
CNNs framework for automatic pulmonary nodule detection in CT scans. ...
ACKNOWLEDGMENT The authors would like to thank Google for providing us with a research grant to run our computations on the Google Cloud Platform and NVIDIA for the support of the GPU. ...
doi:10.1109/tmi.2019.2935553
pmid:31425026
fatcat:tgghe7mvo5bv7o77a27aiztcvq
Study on the detection of pulmonary nodules in CT images based on deep learning
2020
IEEE Access
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Then, the convolution neural network (CNN) optimized by genetic algorithm and the traditional CNN are used to extract the features of CT image of pulmonary nodules. ...
Finally, the CNN optimized by genetic algorithm is used to detect and classify the existing pulmonary nodule images, which provides guidance for CT image detection technology of pulmonary nodule. ...
[13] proposed three-dimensional convolutional neural network 3D CNN to reduce false positives in lung nodule detection. ...
doi:10.1109/access.2020.2984381
fatcat:kbgvx2thfjfcrobocl5esddhwa
A Two-Stage Framework for Automated Malignant Pulmonary Nodule Detection in CT Scans
2020
Diagnostics
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This research is concerned with malignant pulmonary nodule detection (PND) in low-dose CT scans. ...
Using the widely adopted Lung Nodule Analysis dataset (LUNA16), we evaluate the performance of the semantic segmentation stage by adopting two network backbones, namely, MobileNet-V2 and Xception. ...
We propose to employ the recently published DeepLab neural network model in the semantic segmentation of pulmonary nodules in CT scans. ...
doi:10.3390/diagnostics10030131
pmid:32121281
pmcid:PMC7151085
fatcat:wlxwpswi5bfybktfk4trn2cvyy
Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans
[article]
2021
arXiv
pre-print
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Therefore, we proposed a deep learning pipeline, composed of four stages that completely automatized from the detection of nodules to the classification of cancer, through the detection of growth in the ...
In addition, the pipeline integrated a novel approach for nodule growth detection, which relied on a recent hierarchical probabilistic U-Net adapted to report uncertainty estimates. ...
A common approach for automatic nodule detection consists on dividing the problem in two steps [19, 20] : candidate detection and false positive reduction. ...
arXiv:2103.14537v1
fatcat:fcq3acne2fbmbdkb4w7rfcbkxu
Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks
2019
Computerized Medical Imaging and Graphics
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Deep learning techniques have been extensively used in computerized pulmonary nodule analysis in recent years. ...
Our proposed system has four major modules: candidate nodule detection with Faster regional-CNN (R-CNN), candidate merging, false positive (FP) reduction with CNN, and nodule segmentation with customized ...
A nodule detector system typically consists of two steps: 1) candidate detection and 2) false positive reduction. ...
doi:10.1016/j.compmedimag.2019.02.003
pmid:30954678
fatcat:gkyjphqxebbnbfakdeoal2aukm
Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?
2021
Translational Lung Cancer Research
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Lung cancer computed tomography (CT) screening trials using low-dose CT have repeatedly demonstrated a reduction in the number of lung cancer deaths in the screening group compared to a control group. ...
in the analysis of lung screening CT scans. ...
BVG reports grants and stock/royalties from Thirona, and grants and royalties from Delft Imaging Systems, outside ...
doi:10.21037/tlcr-2020-lcs-06
pmid:34164285
pmcid:PMC8182724
fatcat:ytrhhxtxajbpno5m3nimxzjl7a
Front Matter: Volume 10134
2017
Medical Imaging 2017: Computer-Aided Diagnosis
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using a Base 36 numbering system employing both numerals and letters. ...
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model. ...
09 3D convolutional neural network for automatic detection of lung nodules in chest CT 10134 0A Automatic detection of lung nodules: false positive reduction using convolution neural networks and handcrafted ...
doi:10.1117/12.2277119
dblp:conf/micad/X17
fatcat:ika7pheqxngdxejyvkss4dkbv4
3D convolutional neural network for automatic detection of lung nodules in chest CT
2017
Medical Imaging 2017: Computer-Aided Diagnosis
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In this work, we train a 3D CNN for automatic detection of pulmonary nodules in chest CT images using volumes of interest extracted from the LIDC dataset. ...
We then convert the 3D CNN which has a fixed field of view to a 3D fully convolutional network (FCN) which can generate the score map for the entire volume efficiently in a single pass. ...
extraction, false positive reduction, and classification ...
doi:10.1117/12.2255795
pmid:28845077
pmcid:PMC5568782
dblp:conf/micad/HamidianSPP17
fatcat:jeqnjcvanjaodjr7ammori3shq
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge
2017
Medical Image Analysis
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2017 pre-print arXiv:1612.08012v3 fatcat:6r3yw6issvhrvfl6fqwfuhwh6eOther Versions
Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. ...
, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. ...
Database used in this study. ...
doi:10.1016/j.media.2017.06.015
pmid:28732268
fatcat:ur26k5wcizbd3khie3bcqptw6q
Deep Learning for Medical Image Analysis: Applications to Computed Tomography and Magnetic Resonance Imaging
2017
Hanyang Medical Reviews
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In this paper, we will review recent applications of deep learning in the analysis of CT and MR images in a range of tasks and target organs. ...
Recent advances in deep learning have brought many breakthroughs in medical image analysis by providing more robust and consistent tools for the detection, classification and quantification of patterns ...
In lung nodule detection, which is a major target for CADe systems, the task is divided into candidate detection and false positive reduction. ...
doi:10.7599/hmr.2017.37.2.61
fatcat:f4dl4szy35bhfilas3kyblzgui
Machine Learning in Medical Imaging Before and After Introduction of Deep Learning
2017
Medical Imaging and Information Sciences
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It started from an event in 2012 when a deep learning approach based on a convolutional neural network(CNN)won an overwhelming victory in the bestknown worldwide computer-vision competition, ImageNet Classification ...
It is expected that image/pixel-based ML including deep learning will be the mainstream technology in the field of medical imaging in the next few decades. ...
network for medical image pattern recognition, Neural Networks, 8 (7-8) , 1201-1214, 1995. [ 68 ] Lin JS, Lo SB, Hasegawa A, et al. : Reduction of false positives in lung nodule detection using a two-level ...
doi:10.11318/mii.34.14
fatcat:ui5aakxtknac3h2n6fak7chm6q
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