SumNet Convolution Neural network based Automated pulmonary nodule detection system

Jain, Pankaj; Shivwanshi, Resham Raj; Nirala, Neelamshobha; Gupta, Saurabh

doi:10.1109/icatmri51801.2020.9398414

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…The heterogeneity and high variability of nodule imaging characteristics bring significant complexity into this task, and so lung nodule detection can naturally be seen separated in two sub-modules: (1) where multiple candidates are first proposed, and (2) the nodule/non-nodule distinction is refined. Considering DL-based approaches, encoder-decoder architectures are widely used as the base methods for initial nodule detection [78][79][80][81][82][83][84][85]. The extraction of hand-crafted statistical, shape, and texture features also brought valuable information for candidate detection, being further classified by SVM [86,87] or by using ensemble strategies to combine the learning abilities of different classifiers [88].…”

Section: Nodule Detectionmentioning

confidence: 99%

Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges

Silva

Pereira

Neves

et al. 2022

JPM

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Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and “motivate” the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers.

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Section: Nodule Detectionmentioning

confidence: 99%

Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges

Silva

Pereira

Neves

et al. 2022

JPM

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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SumNet Convolution Neural network based Automated pulmonary nodule detection system

Cited by 2 publications

References 14 publications

Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges

Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges

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

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