Automated Pulmonary Nodule Detection: High Sensitivity with Few Candidates

Wang, Bin; Qi, Guo-Jun; Tang, Sheng; Zhang, Liheng; Deng, Licheng; Zhang, Yongdong

doi:10.1007/978-3-030-00934-2_84

Cited by 42 publications

(25 citation statements)

References 9 publications

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“…Although these efforts made good progress in accurately detecting pulmonary nodules from CT scans, the false positive rate is still very high which limits the real application in routine clinical practice. For example, most of the previous work [3,6,11,9] obtained less than 75% sensitivity with 1/8 false positives per scan. To get sensitivity scores as high as 95.8%, these models would bear about eight false positives, which prevent their use in routine clinical practice.…”

Section: Introductionmentioning

confidence: 97%

3DFPN-HS $$^2$$ 2 : 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection

Liu

Cao

Akın

et al. 2019

Lecture Notes in Computer Science

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Accurate detection of pulmonary nodules with high sensitivity and specificity is essential for automatic lung cancer diagnosis from CT scans. Although many deep learning-based algorithms make great progress for improving the accuracy of nodule detection, the high false positive rate is still a challenging problem which limited the automatic diagnosis in routine clinical practice. In this paper, we propose a novel pulmonary nodule detection framework based on a 3D Feature Pyramid Network (3DFPN) to improve the sensitivity of nodule detection by employing multi-scale features to increase the resolution of nodules, as well as a parallel top-down path to transit the high-level semantic features to complement low-level general features. Furthermore, a High Sensitivity and Specificity (HS 2 ) network is introduced to eliminate the falsely detected nodule candidates by tracking the appearance changes in continuous CT slices of each nodule candidate. The proposed framework is evaluated on the public Lung Nodule Analysis (LUNA16) challenge dataset. Our method is able to accurately detect lung nodules at high sensitivity and specificity and achieves 90.4% sensitivity with 1/8 false positive per scan which outperforms the state-of-the-art results 15.6%.

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Section: Introductionmentioning

confidence: 97%

3DFPN-HS $$^2$$ 2 : 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection

Liu

Cao

Akın

et al. 2019

Lecture Notes in Computer Science

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“…However, existing computer aided diagnosis (CAD) systems usually focus on certain types of lesions, e.g. lung nodules [1], focal liver lesions [2], thus their clinical usage is limited. Therefore, a Universal Lesion Detector which can identify and localize different types of lesions across the whole body all at once is in urgent need.…”

Section: Introductionmentioning

confidence: 99%

MVP-Net: Multi-view FPN with Position-Aware Attention for Deep Universal Lesion Detection

Zhang

et al. 2019

Lecture Notes in Computer Science

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Universal lesion detection (ULD) on computed tomography (CT) images is an important but underdeveloped problem. Recently, deep learning-based approaches have been proposed for ULD, aiming to learn representative features from annotated CT data. However, the hunger for data of deep learning models and the scarcity of medical annotation hinders these approaches to advance further. In this paper, we propose to incorporate domain knowledge in clinical practice into the model design of universal lesion detectors. Specifically, as radiologists tend to inspect multiple windows for an accurate diagnosis, we explicitly model this process and propose a multi-view feature pyramid network (FPN), where multi-view features are extracted from images rendered with varied window widths and window levels; to effectively combine this multi-view information, we further propose a position-aware attention module. With the proposed model design, the data-hunger problem is relieved as the learning task is made easier with the correctly induced clinical practice prior. We show promising results with the proposed model, achieving an absolute gain of 5.65% (in the sensitivity of FPs@4.0) over the previous state-of-the-art on the NIH DeepLesion dataset.

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“…While Wang et al [155] proposed a pulmonary detection framework consisting of feature pyramid network, conditional 3D non-maximum suppression, and an attention 3D CNN, Khosravan and Bagci [156] used a single feed-forward pass of a single network for detection, designed as a 3D CNN with dense connections and trained in an end-to-end manner.…”

Section: Lung Cancermentioning

confidence: 99%

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Farhat

Sakr

Kilany

2020

Machine Vision and Applications

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Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend. Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials. Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers. This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19. It covers more than 160 contributions and surveys in this field, all issued between February 2017 and May 2020 inclusively, highlighting various deep learning tasks such as classification, segmentation, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections. It summarizes and discusses the current state-of-the-art approaches in this research domain, highlighting the challenges, especially with COVID-19 pandemic current situation.

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Automated Pulmonary Nodule Detection: High Sensitivity with Few Candidates

Cited by 42 publications

References 9 publications

3DFPN-HS $$^2$$ 2 : 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection

3DFPN-HS $$^2$$ 2 : 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection

MVP-Net: Multi-view FPN with Position-Aware Attention for Deep Universal Lesion Detection

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

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