A semi-supervised convolutional transfer neural network for 3D pulmonary nodules detection

Liu, Mingzhe; Jiang, Xin; Liu, Yanhua; Zhao, Feixiang; Zhou, Helen

doi:10.1016/j.neucom.2018.12.081

Cited by 28 publications

(12 citation statements)

References 9 publications

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“…However, CNN developed slowly until its breakthrough [3], [24] in image classification in 2012. In recent years, CNN has experienced significant advancement in object detection [25], medical image segmentation [26] and action recognition [27]. Various CNN architectures, such as ZFNet [28], VGG [29], GoogLeNet [30], ResNet [31] were designed and the models (weights) of these CNN architectures were obtained by pre-training on largescale datasets, typically the ImageNet [32] and Sports-1M [33].…”

Section: A Convolutional Neural Networkmentioning

confidence: 99%

An Empirical Study of the Convolution Neural Networks Based Detection on Object With Ambiguous Boundary in Remote Sensing Imagery—A Case of Potential Loess Landslide

Yao

Zhou

Liu³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Many objects in the natural environment are generated from the background and even transformed by nature or human beings. Thus, they do not have closed and welldefined boundaries in remote sensing imagery. Recently, CNN (Convolutional Neural Network) based object detection achieved great success in the remote sensing field. However, there is no investigation in the literature about the detection of objects with ambiguous boundaries. In this paper, taking the case of the potential loess landslide detection, we designed massive experiments to evaluate convolutional neural networks for detecting objects with ambiguous boundaries in remote sensing imagery. We analyzed the evaluated methods comprehensively by comparing the performance on objects with ambiguous boundaries in remote sensing imagery with the performance on ordinary objects in visual imagery. Furthermore, drawing from these analyses, we provided a fundamental principle of object representation and a meaningful suggestion of information learning to detect objects with ambiguous boundaries. We finished this paper by presenting several promising directions for detecting objects with ambiguous boundaries to facilitate and spur future research. This paper would provide a significant reference and guidance to develop detectors for objects with ambiguous boundaries in remote sensing imagery.

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Section: A Convolutional Neural Networkmentioning

confidence: 99%

An Empirical Study of the Convolution Neural Networks Based Detection on Object With Ambiguous Boundary in Remote Sensing Imagery—A Case of Potential Loess Landslide

Yao

Zhou

Liu³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

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show abstract

“…In the learning process, it is actually the process of determining the model, as shown in Figure 2 . In this process, feature vectors are first extracted from the data in the training set, which can be textual information, photos, etc., [ 22 ]. The feature vector extraction process is then completed by extracting the desired features from the data.…”

Section: Learning Mode Of Neural Networkmentioning

confidence: 99%

Research on Anomaly Identification and Screening and Metallogenic Prediction Based on Semisupervised Neural Network

Zhang

2022

Computational Intelligence and Neuroscience

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This paper firstly introduces the background of the research on neural network and anomaly identification screening and mineralization prediction under semisupervised learning, then introduces supervised learning, semisupervised learning, unsupervised learning, and reinforcement learning, analyzes and compares their advantages and disadvantages, and concludes that unsupervised learning is the best way to process the data. In the research method, this paper classifies the obtained geochemical data by using semisupervised learning and then trains the obtained samples using the convolutional neural network model to obtain the mineralization prediction model and check its correctness, which finally provides the direction for the subsequent mineralization prediction research.

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“…Setio et al [18] and Liu et al [24] both proposed CNN-based algorithms for pulmonary nodule detection. Setio et al [18] tested their CNN-based program (ConvNets) on cases from the Danish Lung Cancer Screening Trial (DLCST), while Liu et al [24] tested their algorithm on the Kaggle Data Science Bowl 2017 (DSB17) [45]. A third study by Wang et al [26] tested their faster region-CNN (RCNN) based program on cases from an independent database and achieved 75.6% sensitivity on nodule detection.…”

Section: Detection Only (3 Studies)mentioning

confidence: 99%

“…Setio et al [18] trained and tested their algorithm on different types of datasets and achieved a sensitivity of 76.5%, while Liu et al [24] and Wang et al [26] both tested and trained their algorithm on the same type of dataset and achieved a sensitivity of 75.6% and 85.6%, respectively (Tables 1 and 3).…”

Section: Detection Only (3 Studies)mentioning

confidence: 99%

The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Different Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review

Vilmun

Carlsen

et al. 2019

Diagnostics

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The aim of this study was to systematically review the performance of deep learning technology in detecting and classifying pulmonary nodules on computed tomography (CT) scans that were not from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database. Furthermore, we explored the difference in performance when the deep learning technology was applied to test datasets different from the training datasets. Only peer-reviewed, original research articles utilizing deep learning technology were included in this study, and only results from testing on datasets other than the LIDC-IDRI were included. We searched a total of six databases: EMBASE, PubMed, Cochrane Library, the Institute of Electrical and Electronics Engineers, Inc. (IEEE), Scopus, and Web of Science. This resulted in 1782 studies after duplicates were removed, and a total of 26 studies were included in this systematic review. Three studies explored the performance of pulmonary nodule detection only, 16 studies explored the performance of pulmonary nodule classification only, and 7 studies had reports of both pulmonary nodule detection and classification. Three different deep learning architectures were mentioned amongst the included studies: convolutional neural network (CNN), massive training artificial neural network (MTANN), and deep stacked denoising autoencoder extreme learning machine (SDAE-ELM). The studies reached a classification accuracy between 68–99.6% and a detection accuracy between 80.6–94%. Performance of deep learning technology in studies using different test and training datasets was comparable to studies using same type of test and training datasets. In conclusion, deep learning was able to achieve high levels of accuracy, sensitivity, and/or specificity in detecting and/or classifying nodules when applied to pulmonary CT scans not from the LIDC-IDRI database.

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A semi-supervised convolutional transfer neural network for 3D pulmonary nodules detection

Cited by 28 publications

References 9 publications

An Empirical Study of the Convolution Neural Networks Based Detection on Object With Ambiguous Boundary in Remote Sensing Imagery—A Case of Potential Loess Landslide

An Empirical Study of the Convolution Neural Networks Based Detection on Object With Ambiguous Boundary in Remote Sensing Imagery—A Case of Potential Loess Landslide

Research on Anomaly Identification and Screening and Metallogenic Prediction Based on Semisupervised Neural Network

The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Different Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review

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