Deep Active Self-paced Learning for Accurate Pulmonary Nodule Segmentation

Wang, Wenzhe; Lu, Yifei; Wu, Bian; Chen, Tingting; Chen, Danny Z.; Wu, Jian

doi:10.1007/978-3-030-00934-2_80

Cited by 22 publications

(23 citation statements)

References 16 publications

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“…Another approach was done by Wang et al [113] who proposed a deep region-based network (RCNN) for detection of pulmonary nodules in 3D CT images generating simultaneously a segmentation mask for each instance, in addition to a deep active self-paced learning (DASL) strategy for reducing annotation effort and making use of un-annotated samples(weekly supervised).…”

Section: Pulmonary Nodulesmentioning

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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Section: Pulmonary Nodulesmentioning

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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“…Different from the structure used in our preliminary work [21], we increase the cardinality (i.e., the size of the set of transformations) of the last Dense Block and modify the convolutions in [21] into grouped convolutions. The grouping operation thus introduced is more effective than going deeper or wider with limited parameters [25], and can improve the ability of feature extraction.…”

Section: A Nodule-plus R-cnnmentioning

confidence: 99%

“…A preliminary version of this work has been presented at the 2018 MICCAI Conference [21]. In this paper, we extend our method in [21] in the following ways: (1) proposing a novel deep region-based network (Nodule-plus R-CNN) to improve the state-of-the-art pulmonary nodule instance-level segmentation in [21], (2) evaluating and further analyzing the impacts of different SPL schemes, (3) providing a detailed description of our DSAL strategy, and (4) presenting FIGURE 2. Illustration of our weakly-supervised pulmonary nodule instance segmentation strategy.…”

Section: Introductionmentioning

confidence: 99%

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Nodule-Plus R-CNN and Deep Self-Paced Active Learning for 3D Instance Segmentation of Pulmonary Nodules

et al. 2019

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Accurate and automatic segmentation of pulmonary nodules in 3D thoracic Computed Tomography (CT) images is of great significance for Computer-Aided medical Diagnosis (CAD) of lung cancer. Currently, this important task remains challenging for lack of the voxel-level annotation and training strategies that balance target/background voxels in thoracic CT images. In this paper, a new region-based network, called Nodule-plus Region-based CNN, is proposed to detect pulmonary nodules in 3D thoracic CT images effectively while synchronously generating an instance segmentation mask for every detected instance. Our new network is constructed with a stack of convolutional blocks in which lateral connections are used to alleviate the difficulty of vanishing gradients. In addition, in order to reduce annotation workload and make best use of unannotated samples, we proposed a new Deep Self-paced Active Learning (DSAL) strategy by combining Active Learning (AL) and Self-Paced Learning (SPL) strategies. For the purpose of evaluating the performance of our proposed Nodule-plus R-CNN, we conduct a series of experiments on the public LIDC-IDRI dataset, and our model achieves 0.66 Dice and 0.96 TP Dice, which are state-of-the-art best results of pulmonary nodule segmentation. When the amount of available annotated samples is limited, our model trained with the DSAL strategy performs much better than that trained with the standard strategy.

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“…The success achievement widely draws many investigators' attention to apply deep convolution neural networks (CNNs) in medical image analysis. For example, disease classification [6]- [9], lesion segmentation or detection [10]- [13], image registration [14], [15], and so on. In this paper, we explore the classification task of thoracic disease in CXR images using deep learning.…”

Section: Introductionmentioning

confidence: 99%

Learning to Recognize Thoracic Disease in Chest X-Rays With Knowledge-Guided Deep Zoom Neural Networks

et al. 2020

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Automatic and accurate thorax disease diagnosis in Chest X-ray (CXR) image plays an essential role in clinical assist analysis. However, due to its imaging noise regions and the similarity of visual features between diseases and their surroundings, the precise analysis of thoracic disease becomes a challenging problem. In this study, we propose a novel knowledge-guided deep zoom neural network (KGZNet) which is a data-driven model. Our approach leverage prior medical knowledge to guide its training process, due to thoracic diseases typically limit within the lung regions. Also, we utilized weaklysupervised learning (WSL) to search for finer regions without using annotated samples. Learning on each scale consists of a classification sub-network. The KGZNet starts from global images, and iteratively generates discriminative part from coarse to fine; while a finer scale sub-network takes as input an amplified attended discriminative region from previous scales in a recurrent way. Specifically, we first train a robust modified U-Net model of lung segmentation and capture the lung area from the original CXR image through the Lung Region Generator. Then, guided by the attention heatmap, we obtain a finer discriminative lesion region from the lung region images by the Lesion Region Generator. Lastly, the most discriminative features knowledge is fused, and the complementary features information is learned for final disease prediction. Extensive experiments demonstrate that our method can effectively leverage discriminative region information, and significantly outperforms the other state-of-the-art methods in the thoracic disease recognition task.

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Deep Active Self-paced Learning for Accurate Pulmonary Nodule Segmentation

Cited by 22 publications

References 16 publications

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

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

Nodule-Plus R-CNN and Deep Self-Paced Active Learning for 3D Instance Segmentation of Pulmonary Nodules

Learning to Recognize Thoracic Disease in Chest X-Rays With Knowledge-Guided Deep Zoom Neural Networks

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