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Single-view 2D CNNs with Fully Automatic Non-nodule Categorization for False Positive Reduction in Pulmonary Nodule Detection [article]

Hyunjun Eun, Daeyeong Kim, Chanho Jung, Changick Kim
2020 arXiv   pre-print
However, detected candidates include many false positives and thus in the following stage, false positive reduction, such false positives are reliably reduced.  ...  and Objective: In pulmonary nodule detection, the first stage, candidate detection, aims to detect suspicious pulmonary nodules.  ...  Discussion The proposed framework performs false positive reduction in pulmonary nodule detection via single-view 2D CNNs and fully automatic non-nodule categorization.  ... 
arXiv:2003.04454v1 fatcat:2q45e6enuvakti6gyhfrqybsxe

Ensemble Learning of Multiple-View 3D-CNNs Model for Micro-Nodules Identification in CT Images

Patrice Monkam, Shouliang Qi, Mingjie Xu, Haoming Li, Fangfang Han, Yueyang Teng, Wei Qian
2019 IEEE Access  
Moreover, most available systems present high false positive rate resulting from their incapability of discriminating between micro-nodules and non-nodules.  ...  The results demonstrate that developing an automatic system for discriminating between micro-nodules and non-nodules in CT images is feasible, which extends lung cancer studies to micro-nodules.  ...  ACKNOWLEDGMENT The authors would like to express their sincere gratitude to the National Cancer Institute and the Foundation for the National Institutes of Health for their critical role in the creation  ... 
doi:10.1109/access.2018.2889350 fatcat:gzqktx4h7nhbfdkidy7h3uxztm

Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review

Rui Li, Chuda Xiao, Yongzhi Huang, Haseeb Hassan, Bingding Huang
2022 Diagnostics  
Currently, CAD systems for pulmonary nodules comprise data acquisition, pre-processing, lung segmentation, nodule detection, false-positive reduction, segmentation, and classification.  ...  Moreover, this work organizes related literature for classification of parts based on nodule or non-nodule and benign or malignant.  ...  , nodule detection, false-positive reduction, and benign-malignant classification [100] .  ... 
doi:10.3390/diagnostics12020298 pmid:35204388 pmcid:PMC8871398 fatcat:zbasqznr5vblnkfmeuzwlmqbom

Deep learning aided decision support for pulmonary nodules diagnosing: a review

Yixin Yang, Xiaoyi Feng, Wenhao Chi, Zhengyang Li, Wenzhe Duan, Haiping Liu, Wenhua Liang, Wei Wang, Ping Chen, Jianxing He, Bo Liu
2018 Journal of Thoracic Disease  
decision support schemes to effectively tackle the central issues in pulmonary nodules diagnosing, including feature extraction, nodule detection, false-positive reduction, and benign-malignant classification  ...  detection and classification of pulmonary nodules on different formats of images including chest radiographs, computed tomography (CT), and positron emission tomography in the past five decades.  ...  Acknowledgements The authors wish to thank the anonymous referees and Editors of this special issue for their constructive comments.  ... 
doi:10.21037/jtd.2018.02.57 pmid:29780633 pmcid:PMC5945692 fatcat:4zpeokoovrcrho3ukbqs4ovnoq

A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules based on Computed Tomography Scans

Wenming Cao, Rui Wu, Guitao Cao, Zhihai He
2020 IEEE Access  
Low-Dose Computed Tomography (LDCT) screening has been proved as a practical technique for improving the accuracy of pulmonary nodule detection and classification at early cancer diagnosis, which contributes  ...  Due to the extensive use of Convolutional Neural Network (CNN)based methods in pulmonary nodule investigations recently, we summarized the advantages of CNNs over traditional image processing methods.  ...  In this part, we briefly introduce useful algorithms which are developed for candidate nodule detection and false positive reduction.  ... 
doi:10.1109/access.2020.3018666 fatcat:efatgjz7srg5vjfqrx75ttyzbu

A survey of pulmonary nodule detection, segmentation and classification in computed tomography with deep learning techniques

Jianrong Wu, Tianyi Qian
2019 Journal of Medical Artificial Intelligence  
Acknowledgements It is so appreciated for support and help from workmates in the project of Miying in Tencent. Footnote Conflicts of Interest: J Wu and T Qian are Tencent employees.  ...  Detection Generally, nodule detection contains two main stages: (I) nodule candidate generation; (II) false positive reduction (FPR).  ...  It is easy to say that, a fully automatic CADx system could not rely on single classification, and detection is necessary beforehand. Zhu et al.  ... 
doi:10.21037/jmai.2019.04.01 fatcat:s44bw5iwpjf6bpaxqpwi44ysmu

Evolving the pulmonary nodules diagnosis from classical approaches to deep learning aided decision support: three decades development course and future prospect [article]

Bo Liu, Wenhao Chi, Xinran Li, Peng Li, Wenhua Liang, Haiping Liu, Wei Wang, Jianxing He
2019 arXiv   pre-print
the pulmonary nodules with high sensitivity at low false-positives rate as well as on how to precisely differentiate between benign and malignant nodules.  ...  This review also identifies challenges and highlights opportunities for future work in learning models, learning algorithms and enhancement schemes for bridging current state to future prospect and satisfying  ...  Acknowledgements We thank the anonymous referees and editors for their constructive comments in advance on earlier drafts of this paper. We thank Emeritus Prof.  ... 
arXiv:1901.07858v2 fatcat:xxtlnna7vfb7dhybmz3qx2spmq

Radiomics and artificial intelligence in lung cancer screening

Franciszek Binczyk, Wojciech Prazuch, Paweł Bozek, Joanna Polanska
2021 Translational Lung Cancer Research  
The key issue in the fight against this disease is the detection and diagnosis of all pulmonary nodules at an early stage.  ...  This paper illuminates the evolution and current state of artificial intelligence methods in lung imaging and the detection and diagnosis of pulmonary nodules, with a particular emphasis on radiomics and  ...  Then additional 2D CNNs were implemented to minimize false positives in the nodule classification. The last network, serving as the voting node, was used for result fusion.  ... 
doi:10.21037/tlcr-20-708 pmid:33718055 pmcid:PMC7947422 fatcat:qiqnjpiafzfhxeicqlol4v6po4

Cloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT

Anum Masood, Po Yang, Bin Sheng, Huating Li, Ping Li, Jing Qin, Vitaveska Lanfranchi, Jinman Kim, David Dagan Feng
2019 IEEE Journal of Translational Engineering in Health and Medicine  
Lung cancer is a major cause for cancer-related deaths. The detection of pulmonary cancer in the early stages can highly increase survival rate.  ...  This shows the potentials of deep learning, in combination with cloud computing, for accurate and efficient lung nodule detection via CT imaging, which could help doctors and radiologists in treating lung  ...  Volumetric values (3D input) are in correlation with the diameter values (2D input), therefore the combination of both volume and diameter provides divergence for non-nodules. 2) FALSE POSITIVE REDUCTION  ... 
doi:10.1109/jtehm.2019.2955458 pmid:31929952 pmcid:PMC6946021 fatcat:lc2u32z5lncnpcbbunsngw77ay

Deep Learning for Lung Cancer Nodules Detection and Classification in CT Scans

Diego Riquelme, Moulay A. Akhloufi
2020 AI  
They are divided into two categories—(1) Nodule detection systems, which from the original CT scan detect candidate nodules; and (2) False positive reduction systems, which from a set of given candidate  ...  Detecting malignant lung nodules from computed tomography (CT) scans is a hard and time-consuming task for radiologists.  ...  A boosting architecture based on 2D CNN is used for false positive reduction.  ... 
doi:10.3390/ai1010003 fatcat:k7mj3cdzovhhpkxknm34zsw7c4

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Hanan Farhat, George E. Sakr, Rima Kilany
2020 Machine Vision and Applications  
, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections.  ...  Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers.  ...  Yet, for precision, CONAF recorded 0.21 for lesions vs. normal only and 0.15 for lesion vs. all others. For the sake of false positive reduction in automated pulmonary nodule detection, Dou et al.  ... 
doi:10.1007/s00138-020-01101-5 pmid:32834523 pmcid:PMC7386599 fatcat:tkkylrptc5hkpoj52hjs3kuttu

Automatic Detection of Pulmonary Nodules using Three-dimensional Chain Coding and Optimized Random Forest

May Phu Paing, Kazuhiko Hamamoto, Supan Tungjitkusolmun, Sarinporn Visitsattapongse, Chuchart Pintavirooj
2020 Applied Sciences  
The detection of pulmonary nodules on computed tomography scans provides a clue for the early diagnosis of lung cancer.  ...  0.0863 false positives per exam.  ...  CT scans that are applied in this research.  ... 
doi:10.3390/app10072346 fatcat:5upq4erk65fhbjmcac7fssqdoe

A Joint Detection and Recognition Approach to Lung Cancer Diagnosis from CT images with Label Uncertainty

Chenyang Liu, Shing-Chow Chan
2020 IEEE Access  
The false-positive reduction is another essential step after nodule detection to eliminate false positive candidates, and 3-D CNN is usually preferred [4, 8, 32, 33] because of their excellent performance  ...  Moreover, the false-positive nodules are labelled as non-nodule with probability 1. The network is then trained jointly.  ... 
doi:10.1109/access.2020.3044941 fatcat:wdmct6ix6rbotcndlnndnxraaa

AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics [article]

Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar, Su Ruan, Babak Saboury, Arman Rahmim
2022 arXiv   pre-print
This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks.  ...  AI-based detection searches the image space to find the regions of interest based on patterns and features.  ...  Acknowledgements This project was in part supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant RGPIN-2019-06467, and the Canadian Institutes of Health Research  ... 
arXiv:2110.10332v4 fatcat:vmpxhoolarbrve5ddyfn5umfim

Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges

Tanzila Saba
2020 Journal of Infection and Public Health  
Several state of art techniques are categorized under the same cluster and results are compared on benchmark datasets from accuracy, sensitivity, specificity, false-positive metrics.  ...  Cancer also known as tumor must be quickly and correctly detected in the initial stage to identify what might be beneficial for its cure.  ...  Additionally author is thankful to the anonymous reviewers for their constructive comments and apologize to those researchers whom work is overlooked in this research.  ... 
doi:10.1016/j.jiph.2020.06.033 pmid:32758393 fatcat:sglazth4znh5jjtozguaktruce
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