3D deep learning for detecting pulmonary nodules in CT scans

Gruetzemacher, Ross; Gupta, Ashish; Paradice, David

doi:10.1093/jamia/ocy098

Cited by 71 publications

(36 citation statements)

References 57 publications

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“…They proposed a novel approach of identifying discriminative regions called landmarks, followed by application of a CNN for patch-based deep feature learning [10]. Addressing challenges in other common imaging modalities, Gruezemacher et al, developed a three-dimensional (3D) adaptation of deep neural nets (DNNs) for lung nodule detection from computed tomography (CT) images [6]; Hassan et al, presented a novel approach combining tensor-based segmentation, Delauday triangulation, and deep learning models to provide complete 3D presentation of macula to support automated diagnosis of various macula conditions [7].…”

Section: Discussionmentioning

confidence: 99%

“…Section editors screened this initial list for relevance to the theme and scientific quality, and they rated each paper as "keep," "pend," or "discard." Papers rated as "keep" by one of the section editors were independently reviewed and scored by section editors to yield the top 15 candidate best papers [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Criteria for scoring included innovation beyond established AI techniques, work that addressed substantial challenges in the field, and rigorous scientific evaluations.…”

Section: Paper Selection Methodsmentioning

confidence: 99%

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Artificial Intelligence in Health in 2018: New Opportunities, Challenges, and Practical Implications

Jackson

Hu²

2019

Yearb Med Inform

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Objective: To summarize significant research contributions to the field of artificial intelligence (AI) in health in 2018. Methods: Ovid MEDLINE® and Web of Science® databases were searched to identify original research articles that were published in the English language during 2018 and presented advances in the science of AI applied in health. Queries employed Medical Subject Heading (MeSH®) terms and keywords representing AI methodologies and limited results to health applications. Section editors selected 15 best paper candidates that underwent peer review by internationally renowned domain experts. Final best papers were selected by the editorial board of the 2018 International Medical Informatics Association (IMIA) Yearbook. Results: Database searches returned 1,480 unique publications. Best papers employed innovative AI techniques that incorporated domain knowledge or explored approaches to support distributed or federated learning. All top-ranked papers incorporated novel approaches to advance the science of AI in health and included rigorous evaluations of their methodologies. Conclusions: Performance of state-of-the-art AI machine learning algorithms can be enhanced by approaches that employ a multidisciplinary biomedical informatics pipeline to incorporate domain knowledge and can overcome challenges such as sparse, missing, or inconsistent data. Innovative training heuristics and encryption techniques may support distributed learning with preservation of privacy.

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Section: Discussionmentioning

confidence: 99%

Section: Paper Selection Methodsmentioning

confidence: 99%

Artificial Intelligence in Health in 2018: New Opportunities, Challenges, and Practical Implications

Jackson

Hu²

2019

Yearb Med Inform

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“…The proposed system in Reference [78] uses two 3D deep learning models, one for candidate generation and the other for false positive reduction. An overview of the pipeline is shown in Figure 10.…”

Section: D Deep Learning Approachesmentioning

confidence: 99%

Deep Learning for Lung Cancer Nodules Detection and Classification in CT Scans

Riquelme

Akhloufi

2020

126

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Detecting malignant lung nodules from computed tomography (CT) scans is a hard and time-consuming task for radiologists. To alleviate this burden, computer-aided diagnosis (CAD) systems have been proposed. In recent years, deep learning approaches have shown impressive results outperforming classical methods in various fields. Nowadays, researchers are trying different deep learning techniques to increase the performance of CAD systems in lung cancer screening with computed tomography. In this work, we review recent state-of-the-art deep learning algorithms and architectures proposed as CAD systems for lung cancer detection. They are divided into two categories—(1) Nodule detection systems, which from the original CT scan detect candidate nodules; and (2) False positive reduction systems, which from a set of given candidate nodules classify them into benign or malignant tumors. The main characteristics of the different techniques are presented, and their performance is analyzed. The CT lung datasets available for research are also introduced. Comparison between the different techniques is presented and discussed.

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“…Nodule detection was achieved using a 3-D fast regional CNN (R-CNN), and the system achieved a relatively lower detection sensitivity of 83.4%. Gruetzemacher et al 90 proposed a lung nodule detection method using two 3-D CNNs: the first was used to generate candidate nodules and the second was used to reduce FPs. Using 888 scans from the LIDC dataset, a sensitivity of 89.29% was demonstrated with 1.78 FPs/scans.…”

Section: 2012mentioning

confidence: 99%

Computer-aided detection of lung nodules: a review

2019

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We present an in-depth review and analysis of salient methods for computer-aided detection of lung nodules. We evaluate the current methods for detecting lung nodules using literature searches with selection criteria based on validation dataset types, nodule sizes, numbers of cases, types of nodules, extracted features in traditional feature-based classifiers, sensitivity, and false positives (FP)/scans. Our review shows that current detection systems are often optimized for particular datasets and can detect only one or two types of nodules. We conclude that, in addition to achieving high sensitivity and reduced FP/scans, strategies for detecting lung nodules must detect a variety of nodules with high precision to improve the performances of the radiologists. To the best of our knowledge, ours is the first review of the effectiveness of feature extraction using traditional feature-based classifiers. Moreover, we discuss deep-learning methods in detail and conclude that features must be appropriately selected to improve the overall accuracy of the system. We present an analysis of current schemes and highlight constraints and future research areas.

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3D deep learning for detecting pulmonary nodules in CT scans

Abstract: A novel deep-neural-network-based pulmonary nodule detection system is demonstrated and validated. The results provide comparison of the proposed deep-learning-based system over other similar systems based on performance.

Cited by 71 publications

References 57 publications

Artificial Intelligence in Health in 2018: New Opportunities, Challenges, and Practical Implications

Artificial Intelligence in Health in 2018: New Opportunities, Challenges, and Practical Implications

Deep Learning for Lung Cancer Nodules Detection and Classification in CT Scans

Computer-aided detection of lung nodules: a review

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