No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting With Adversarial Attacks

Liu, Siqi; Setio, Arnaud Arindra Adiyoso; Ghesu, Florin C.; Gibson, Eli; Grbić, Saša; Georgescu, Bogdan; Comanicìu, Dorin

doi:10.1109/tmi.2020.3026261

Cited by 35 publications

(31 citation statements)

References 34 publications

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“…The adversarial augmentation approach was proposed in [ 60 ], which was used to generalize the model. Project gradient descent (PGD) or adverse synthetic nodule and adverse perturbation noise work detected the lung by false positive reduction (FPR).…”

Section: Related Workmentioning

confidence: 99%

Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition

Lal

Rehman

Shah

et al. 2021

Sensors

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Due to the rapid growth in artificial intelligence (AI) and deep learning (DL) approaches, the security and robustness of the deployed algorithms need to be guaranteed. The security susceptibility of the DL algorithms to adversarial examples has been widely acknowledged. The artificially created examples will lead to different instances negatively identified by the DL models that are humanly considered benign. Practical application in actual physical scenarios with adversarial threats shows their features. Thus, adversarial attacks and defense, including machine learning and its reliability, have drawn growing interest and, in recent years, has been a hot topic of research. We introduce a framework that provides a defensive model against the adversarial speckle-noise attack, the adversarial training, and a feature fusion strategy, which preserves the classification with correct labelling. We evaluate and analyze the adversarial attacks and defenses on the retinal fundus images for the Diabetic Retinopathy recognition problem, which is considered a state-of-the-art endeavor. Results obtained on the retinal fundus images, which are prone to adversarial attacks, are 99% accurate and prove that the proposed defensive model is robust.

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Section: Related Workmentioning

confidence: 99%

Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition

Lal

Rehman

Shah

et al. 2021

Sensors

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“…Azad et al [68] developed a Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), which use different ways of concatenation to take full advantages of multiple feature maps for lung nodule recognition and segmentation. Besides, some fusion networks are also explored using multi-stream structures in order to integrate the power of different networks [25], [65], [80]- [82]. Liu et al [80] exploited three identical 3D ResUNets to generate 3D Gaussian blob nodules, then fine-tuned the network by adding 3D RPN heads resulting in higher sensitivity on large nodules.…”

Section: ) Candidate Nodule Detectionmentioning

confidence: 99%

“…Besides, some fusion networks are also explored using multi-stream structures in order to integrate the power of different networks [25], [65], [80]- [82]. Liu et al [80] exploited three identical 3D ResUNets to generate 3D Gaussian blob nodules, then fine-tuned the network by adding 3D RPN heads resulting in higher sensitivity on large nodules.…”

Section: ) Candidate Nodule Detectionmentioning

confidence: 99%

“…In recent years, massive DNN-based methods, specifically various CNNs, have been proposed to enhance classification performance. According to the difference of network structures, we can categorize these networks into advanced off-the-shelf CNNs [24], [51], [54], [67], [76], [78]- [80], [94], [100], [105], [108], and multi-stream heterogeneous CNNs [53], [58], [72], [75], [85], [86]- [88]. Liu et al [76] developed a High Sensitivity and Specificity [80]can be used to improve performance for better classification.…”

Section: ) False Positive Reductionmentioning

confidence: 99%

“…For example, At Liu et all. [80] applied Projected Gradient Descent to generate three types of adversarial samples, then trained a 3D Dense U-Net with the extracted candidates and adversarial samples, resulting in a 5.33% improvement on CPM.…”

Section: ) False Positive Reductionmentioning

confidence: 99%

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

Cao

et al. 2020

IEEE Access

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Lung cancer is one of the malignant tumor diseases with the fastest increase in morbidity and mortality, which poses a great threat to human health. 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 to mortality reduction. 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. In this paper, a comprehensive review of the application and development of CAD systems is presented. 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. We then introduce the main structure of a CAD system and present some efficient methodologies. 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. Besides, we mainly select the CAD systems developed by state-of-theart CNNs with excellent performance and analyze their objectives, algorithms as well as results. Finally, research trends, existing challenges, and future directions in this field are discussed.

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Improving lung cancer detection using faster region‐based convolutional neural network aided with fuzzy butterfly optimization algorithm

Sinthia

Malathi²,

Anitha

et al. 2022

Concurrency and Computation

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Lung cancer is the most deadly type of cancer and it is caused by genetic variations in lung tissues. Other causes of lung cancer are alcohol, smoking, and threatening gas exposures. The diagnosis of lung cancer is an intricate task and early detection of lung cancer can help to get the exact treatment in advance. The application of a computer-aided diagnosis process helps to predict lung cancer earlier, nonetheless, it does not provide better accuracy. The overfitting nature of features and dimensionality of lung cancer can prevent it from obtaining maximum accuracy. Hence, we proposed a novel faster region convolutional neural network (RCNN) based fuzzy butterfly optimization algorithm (FBOA) to achieve better prediction accuracy and effectiveness. The proposed Faster RCNN can provide better positioning of lung cancer swiftly and effectively and the FBOA approach can be used to perform two-stage classification. The fuzzy rules used in the FBOA can be utilized to find the severity of the lung cancer and differentiate the Benign stage and Malignant stage effectively.The experimental analyses are performed in MATLAB simulation. The preprocessing of images is performed by different tools from MATLAB and is to format the images as required. The cancer imaging archive (TCIA) dataset is utilized to analyze the performance of the proposed method and compared with various state-of-art works. The performance of the proposed method is evaluated by using different evaluation metrics such as precision, recall, F-measure, and accuracy and attained 99, 98, 99, and 97% respectively. Thus, our proposed method outperforms all the other approaches.

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No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting With Adversarial Attacks

Cited by 35 publications

References 34 publications

Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition

Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition

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

Improving lung cancer detection using faster region‐based convolutional neural network aided with fuzzy butterfly optimization algorithm

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