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A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules based on Computed Tomography Scans
2020
IEEE Access
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The experimental benchmarks for nodule analysis are first described and summarized, covering public datasets of lung CT scans, commonly used evaluation metrics, and various medical competitions. ...
Therefore, with the explosive growth of CT data, it is of great clinical significance to exploit an effective Computer-Aided Diagnosis (CAD) system for radiologists on automatic nodule analysis. ...
[61] proposed a multi-task 2D CNN with Margin Ranking loss (MTMR-Net) to construct the CADx system for nodule analysis. ...
doi:10.1109/access.2020.3018666
fatcat:efatgjz7srg5vjfqrx75ttyzbu
Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning
[chapter]
2017
Lecture Notes in Computer Science
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2017 pre-print arXiv:1704.08797v1 fatcat:pkvwnibrxjeajk72p3lxwqs5fu
Risk stratification of lung nodules is a task of primary importance in lung cancer diagnosis. ...
Moreover, we also acquire the task dependent feature representation for six high-level nodule attributes and fuse this complementary information via a Multi-task learning (MTL) framework. ...
[7] first segmented the lung nodules using appearance-based models and used spherical harmonic analysis to perform shape analysis. The final step was the classification using k-nearest neighbor. ...
doi:10.1007/978-3-319-59050-9_20
fatcat:oq23wasdgrggpevudeg5c5a3ei
Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches
2019
IEEE Transactions on Medical Imaging
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2018 pre-print arXiv:1801.03230v2 fatcat:vod6h4d3bjh4xlq2vd43qkwnjuOther Versions
Motivated by the radiologists' interpretations of the scans, we then show how to incorporate task-dependent feature representations into a CAD system via a graph-regularized sparse multi-task learning ...
Our first approach is based on supervised learning for which we demonstrate significant gains with deep learning algorithms, particularly by utilizing a 3D convolutional neural network and transfer learning ...
Following up on the application of deep learning for almost all tasks in the visual domain, we studied the influence of different pre-trained deep networks for lung nodule classification. ...
doi:10.1109/tmi.2019.2894349
pmid:30676950
fatcat:woorhrucqjbcxhulknvh2irjta
Attention-Enhanced Cross-Task Network for Analysing Multiple Attributes of Lung Nodules in CT
[article]
2021
arXiv
pre-print
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Current state-of-the-art methods that score multiple attributes rely on deep learning-based multi-task learning (MTL) schemes. ...
Our model also performs competitively when repurposed for benign-malignant classification. ...
[12] reported Multi-Task deep model with Margin Ranking loss (MTMR-Net), which leveraged the Siamese network architecture with a 152-layer ResNet [15] backbone to simultaneously score attributes and ...
arXiv:2103.03931v2
fatcat:qxpewluygfbc5d4ueti5fun27i
A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2004.12150v2 fatcat:j2hrsapa5jejhef2u3lhmedrbeOther Versions
Although deep learning models like CNNs have achieved great success in medical image analysis, the small size of medical datasets remains a major bottleneck in this area. ...
In this survey, we summarize the current progress on integrating medical domain knowledge into deep learning models for various tasks, such as disease diagnosis, lesion, organ and abnormality detection ...
The multi-task learning (MTL) framework is proposed to incorporate the above information into the main task of lung nodule classification. ...
arXiv:2004.12150v3
fatcat:2cqumcjkizgivmo67reznxacie
A survey of pulmonary nodule detection, segmentation and classification in computed tomography with deep learning techniques
2019
Journal of Medical Artificial Intelligence
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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. ...
LUNA16 The LUNA16 dataset was created for the challenge of LUng Nodule Analysis 2016 including 888 CT scans, which were gathered from LIDC-IDRI with slice thickness less than 3mm. ...
(37) propose a method for pulmonary nodule detection utilizing multi-group patches cut out from the lung images. ...
doi:10.21037/jmai.2019.04.01
fatcat:s44bw5iwpjf6bpaxqpwi44ysmu
Multi-Level Cross Residual Network for Lung Nodule Classification
2020
Sensors
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nodules) of lung nodules, respectively. ...
To illustrate the performance of ML-xResNet, we apply the model to process ternary classification (benign, indeterminate, and malignant lung nodules) and binary classification (benign and malignant lung ...
Then, they applied a Siamese network with a margin loss to learning to rank the most malignancy-related features to improve the distinguishing ability of the network. ...
doi:10.3390/s20102837
pmid:32429401
fatcat:wfgbc4r3bzcwlgz7hasv4yz4na
Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT
2018
IEEE Transactions on Medical Imaging
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The penalty loss function is used for better reduction of the false negative rate with a minimal effect on the overall performance of the MV-KBC model. ...
In this paper, we propose a multi-view knowledge-based collaborative (MV-KBC) deep model to separate malignant from benign nodules using limited chest CT data. ...
[31] proposed 3D CNN multi-task learning for lung nodule characterization. ...
doi:10.1109/tmi.2018.2876510
pmid:30334786
fatcat:vstymhfyijgshfsk24b6ckrbna
Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis
2021
IEEE Transactions on Pattern Analysis and Machine Intelligence
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On the LIDC-IDRI benchmark dataset for benign-malignant classification of pulmonary nodules in CT images, our method achieves a new state-of-the-art accuracy of 95.36% and an AUC of 96.54%. ...
We have successfully applied our hybrid reasoning algorithm to two challenging medical image diagnosis tasks. ...
'B' And 'M' are the Number of Benign and Malignant Lung Nodules Used for Model Training. ...
doi:10.1109/tpami.2021.3130759
pmid:34822325
fatcat:obh3bcyf65a6dcnc47svv23zpu
The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification
[article]
2021
arXiv
pre-print
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In lung nodule classification, for example, many works report results on the publicly available LIDC dataset. ...
with respect to very simple baseline methods, highlighting that the selected data distribution may play an even more important role than the model architecture. ...
1040
84.20
NoduleX [3]
394
270
93.20
Multi-crop CNN [17]
880
495
87.14
Multi-task w/ margin ranking loss [8]
972
450
93.50 ...
arXiv:2108.05386v1
fatcat:l7ahpm25jzg4zjoub2rhppwrxu
Combating Ambiguity for Hash-code Learning in Medical Instance Retrieval
[article]
2021
arXiv
pre-print
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The segmentation loss allows exploring subtle spatial differences for good spatial-discriminability while the classification loss utilizes class-aware semantic information for good semantic-separability ...
Y-Net can learn highly discriminative convolutional features by unifying the pixel-wise segmentation loss and classification loss. ...
All deep models are trained from scratch with 500 epochs. It spends approximately 3 hours for training our Y-Net. The pixel-wise cross-entropy loss is used in the segmentation task. ...
arXiv:2105.08872v1
fatcat:5ga4eudoorchja5xwswm453jje
Attention-Guided Feature Extraction and Multiscale Feature Fusion 3D ResNet for Automated Pulmonary Nodule Detection
2022
IEEE Access
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Automatic detection of pulmonary nodules is critical for the early diagnosis and prevention of lung cancer. Computed tomography (CT) is an effective and economical lung cancer detection method. ...
Therefore, the automatic localization of pulmonary nodules in CT images is a challenging task. ...
LOSS FUNCTION Candidate nodule detection and nodule classification prediction share a 3D residual network, and the lung nodule detection model is a multi-task learning model.Our loss function is composed ...
doi:10.1109/access.2022.3182104
fatcat:l33qo4oal5f4vfo6i5gmbm536i
Ensemble Learning of Multiple-View 3D-CNNs Model for Micro-Nodules Identification in CT Images
2019
IEEE Access
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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. ...
Then, five distinct 3D-CNN models are built and implemented on one size of the nodule candidates. ...
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
Cascaded-Recalibrated Multiple Instance Deep Model for Pathologic-Level Lung Cancer Prediction in CT Images
2022
Computational Intelligence and Neuroscience
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MIL model provides substantial improvements for the pathologic-level lung cancer prediction by using the CT images. ...
This cascaded-recalibrated MIL deep model incorporates a cascaded recalibration mechanism at the nodule level and attribute level, which fuses the informative attribute features into nodule embeddings ...
University of Science and Technology under Grants 19zx7143 and 20zx7137, in part by the Bethune Charitable Foundation, under Grant J202002E001. e Grants 19zx7143 and 20zx7137 provided financial support for ...
doi:10.1155/2022/9469234
pmid:35733559
pmcid:PMC9208922
fatcat:e6owqfad25hypfwyl45ro7ntly
A survey on deep learning in medical image analysis
2017
Medical Image Analysis
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2017 pre-print arXiv:1702.05747v1 fatcat:zdedepkenraglidf7b4ojxcohiOther Versions
We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. ...
This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. ...
Acknowledgments The authors would like to thank members of the Diagnostic Image Analysis Group for discussions and suggestions. ...
doi:10.1016/j.media.2017.07.005
pmid:28778026
fatcat:esbj72ftwvbgzh6jgw367k73j4
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