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Lymph node detection in MR Lymphography: false positive reduction using multi-view convolutional neural networks

Oscar A. Debats, Geert J.S. Litjens, Henkjan J. Huisman
2019 PeerJ  
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]

Zhongliu Xie
2018 arXiv   pre-print
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

Zhitao Xiao, Naichao Du, Lei Geng, Fang Zhang, Jun Wu, Yanbei Liu
2019 Applied Sciences  
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]

Sunyi Zheng, Jiapan Guo, Xiaonan Cui, Raymond N. J. Veldhuis, Matthijs Oudkerk, Peter M.A.van Ooijen
2019 arXiv   pre-print
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

Sunyi Zheng, Jiapan Guo, Xiaonan Cui, Raymond N. J. Veldhuis, Matthijs Oudkerk, Peter M.A. van Ooijen
2019 IEEE Transactions on Medical Imaging  
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

Gai Li, Wei Zhou, Weibin Chen, Fengtao Sun, Yu Fu, Fengling Gong, Huiying Zhang
2020 IEEE Access  
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

Shimaa EL-Bana, Ahmad Al-Kabbany, Maha Sharkas
2020 Diagnostics  
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]

Xavier Rafael-Palou
2021 arXiv   pre-print
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

Xia Huang, Wenqing Sun, Tzu-Liang (Bill) Tseng, Chunqiang Li, Wei Qian
2019 Computerized Medical Imaging and Graphics  
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?

Anton Schreuder, Ernst T. Scholten, Bram van Ginneken, Colin Jacobs
2021 Translational Lung Cancer Research  
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  
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

Sardar Hamidian, Berkman Sahiner, Nicholas Petrick, Aria Pezeshk, Nicholas A. Petrick, Samuel G. Armato
2017 Medical Imaging 2017: Computer-Aided Diagnosis  
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

Arnaud Arindra Adiyoso Setio, Alberto Traverso, Thomas de Bel, Moira S.N. Berens, Cas van den Bogaard, Piergiorgio Cerello, Hao Chen, Qi Dou, Maria Evelina Fantacci, Bram Geurts, Robbert van der Gugten, Pheng Ann Heng (+20 others)
2017 Medical Image Analysis  
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

Kyu-Hwan Jung, Hyunho Park, Woochan Hwang
2017 Hanyang Medical Reviews  
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

Kenji SUZUKI
2017 Medical Imaging and Information Sciences  
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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