Pulmonary Nodule Detection Techniques in CT Images: New Strategies and Challenges

Jacob, Chinnu; Gopakumar, C.

doi:10.1109/icaccs48705.2020.9074161

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Radiologists often generate pulmonary nodule misdiagnoses or leak diagnoses (Miki et al 2021) of small lesions from spatial patterns. GGOs and juxta-pleura lead to less precise assessments (Jacob and Gopakumar 2020, Solomon et al 2021, Xiao et al 2021, Zheng et al 2021.…”

Section: Poor Generalization Capability Of Independent Samplesmentioning

confidence: 99%

Computer-aided detection of pulmonary nodules based on convolutional neural networks: a review

Min¹,

Hu²,

Long³

et al. 2022

Phys. Med. Biol.

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Computer-aided detection (CADe) technology has been proven to increase the detection rate of pulmonary nodules that has important clinical significance for the early diagnosis of lung cancer. In this study, we systematically review the latest techniques in pulmonary nodule CADe based on deep learning models with convolutional neural networks in computed tomography images. First, the brief descriptions and popular architecture of convolutional neural networks are introduced. Second, several common public databases and evaluation metrics are briefly described. Third, state-of-the-art approaches with excellent performances are selected. Subsequently, we combine the clinical diagnostic process and the traditional four steps of pulmonary nodule CADe into two stages, namely, data preprocessing and image analysis. Further, the major optimizations of deep learning models and algorithms are highlighted according to the progressive evaluation effect of each method, and some clinical evidence is added. Finally, various methods are summarized and compared. The innovative or valuable contributions of each method are expected to guide future research directions. The analyzed results show that deep learning-based methods significantly transformed the detection of pulmonary nodules, and the design of these methods can be inspired by clinical imaging diagnostic procedures. Moreover, focusing on the image analysis stage will result in improved returns. In particular, optimal results can be achieved by optimizing the steps of candidate nodule generation and false positive reduction. End-to-end methods, with greater operating speeds and lower computational consumptions, are superior to other methods in CADe of pulmonary nodules.

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Section: Poor Generalization Capability Of Independent Samplesmentioning

confidence: 99%

Computer-aided detection of pulmonary nodules based on convolutional neural networks: a review

Min¹,

Hu²,

Long³

et al. 2022

Phys. Med. Biol.

View full text Add to dashboard Cite

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Federated learning: a deep learning model based on resnet18 dual path for lung nodule detection

Liu

Fan²,

Yang³

2022

Multimed Tools Appl

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Lung nodule detection is of vital importance in the prevention of lung cancer. In the past two decades, most machine learning and deep learning approaches have focused on training models using data collected and stored in centralised data repositories. However, as privacy security becoming more and more important, patient data is scattered in different medical institutions on a small scale and fragmented. In this study, we proposed a federated learning method for training a lung nodule detection model on horizontally distributed data from different clients. In particular, the federated averaging algorithm is used to detect lung nodules by proposing a 3D ResNet18 Dual Path Faster R-CNN model. On this basis, we firstly considered that the quality of the data affects the model training effect. Therefore, we proposed a sampling-based content diversity algorithm that is validated on luna16 data, mitigating model overfitting and improving model generalisation with better results, and also reducing the training time of model. In order to further verify 3D ResNet18 Dual Path Faster R-CNN of federated learning algorithm, we compared it with other federated learning algorithms of deep learning. The experimental results show that the 3D ResNet18 Dual Path Faster R-CNN of federated learning algorithm achieves the best results.

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Pulmonary Nodule Detection Techniques in CT Images: New Strategies and Challenges

Cited by 2 publications

References 23 publications

Computer-aided detection of pulmonary nodules based on convolutional neural networks: a review

Computer-aided detection of pulmonary nodules based on convolutional neural networks: a review

Federated learning: a deep learning model based on resnet18 dual path for lung nodule detection

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