Gated-SCNN: Gated Shape CNNs for Semantic Segmentation

Takikawa, Towaki; Acuna, David; Jampani, Varun; Fidler, Sanja

doi:10.1109/iccv.2019.00533

Cited by 655 publications

(446 citation statements)

References 56 publications

(78 reference statements)

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“…For example, for nodule detection we can use the new YOLO v3 [102] which adds some scale invariance and can help detect lung nodules of different sizes. New segmentation techniques such as DeepLabv3+ [103] and Gated-SCNN [104] can also be used to extract nodules candidates. The classification of the nodule into malignant or benign can benefit from the performance of the recently proposed deep EfficientNet architectures [105].…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

Riquelme

Akhloufi

2020

126

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“…This flexible solution can be easily adapted to work with different semantic segmentation architecture. Recently, the GATED SCNN [36] demonstrated to output sharper predicted areas and achieves more robust performance on smaller objects.…”

Section: Discussionmentioning

confidence: 99%

On Improving the Training of Models for the Semantic Segmentation of Benthic Communities from Orthographic Imagery

et al. 2020

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The semantic segmentation of underwater imagery is an important step in the ecological analysis of coral habitats. To date, scientists produce fine-scale area annotations manually, an exceptionally time-consuming task that could be efficiently automatized by modern CNNs. This paper extends our previous work presented at the 3DUW’19 conference, outlining the workflow for the automated annotation of imagery from the first step of dataset preparation, to the last step of prediction reassembly. In particular, we propose an ecologically inspired strategy for an efficient dataset partition, an over-sampling methodology targeted on ortho-imagery, and a score fusion strategy. We also investigate the use of different loss functions in the optimization of a Deeplab V3+ model, to mitigate the class-imbalance problem and improve prediction accuracy on coral instance boundaries. The experimental results demonstrate the effectiveness of the ecologically inspired split in improving model performance, and quantify the advantages and limitations of the proposed over-sampling strategy. The extensive comparison of the loss functions gives numerous insights on the segmentation task; the Focal Tversky, typically used in the context of medical imaging (but not in remote sensing), results in the most convenient choice. By improving the accuracy of automated ortho image processing, the results presented here promise to meet the fundamental challenge of increasing the spatial and temporal scale of coral reef research, allowing researchers greater predictive ability to better manage coral reef resilience in the context of a changing environment.

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“…The visual comparisons contain the comparison between MWEN "with MTFE" and "without MTFE" and the comparison between MWEN, FCN, Unet, and Deeplab V3+ on regions with different types of surface water bodies and confusing objects. Regarding the evaluation metrics, five evaluation metrics are used to evaluate the accuracy in this study, including the Overall Accuracy (OA) [30], the True Water Rate (TWR), the False Water Rate(FWR), the Water Intersection over Union (WIoU) [30], and the Mean Intersection over Union (MIoU) [39]. The definitions and formulas of these indicators are listed in Table 2.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

A Multi-Scale Water Extraction Convolutional Neural Network (MWEN) Method for GaoFen-1 Remote Sensing Images

Guo

Jian

et al. 2020

IJGI

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Automatic water body extraction method is important for monitoring floods, droughts, and water resources. In this study, a new semantic segmentation convolutional neural network named the multi-scale water extraction convolutional neural network (MWEN) is proposed to automatically extract water bodies from GaoFen-1 (GF-1) remote sensing images. Three convolutional neural networks for semantic segmentation (fully convolutional network (FCN), Unet, and Deeplab V3+) are employed to compare with the water bodies extraction performance of MWEN. Visual comparison and five evaluation metrics are used to evaluate the performance of these convolutional neural networks (CNNs). The results show the following. (1) The results of water body extraction in multiple scenes using the MWEN are better than those of the other comparison methods based on the indicators. (2) The MWEN method has the capability to accurately extract various types of water bodies, such as urban water bodies, open ponds, and plateau lakes. (3) By fusing features extracted at different scales, the MWEN has the capability to extract water bodies with different sizes and suppress noise, such as building shadows and highways. Therefore, MWEN is a robust water extraction algorithm for GaoFen-1 satellite images and has the potential to conduct water body mapping with multisource high-resolution satellite remote sensing data.

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Gated-SCNN: Gated Shape CNNs for Semantic Segmentation

Cited by 655 publications

References 56 publications

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

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

On Improving the Training of Models for the Semantic Segmentation of Benthic Communities from Orthographic Imagery

A Multi-Scale Water Extraction Convolutional Neural Network (MWEN) Method for GaoFen-1 Remote Sensing Images

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