Cascaded atrous dual attention U-Net for tumor segmentation

Liu, Yucheng; Shahid, Mohammad; Sarapugdi, Wannaporn; Yong, Lin; Chen, Jyh Cheng; Hua, Kai

doi:10.1007/s11042-020-10078-2

Cited by 12 publications

(10 citation statements)

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“…Since then, a mass of variants of U-Net have been proposed to boost its performance in various kinds of medical scenarios, including but not limited to improvements to skip pathways [3,4], more powerful and sophisticated U-blocks [5][6][7][8][9], cascaded U-structures [10][11][12][13][14][15][16], combining with attention mechanism [17][18][19][20] and so on.…”

Section: Introductionmentioning

confidence: 99%

“…The most intuitive and simplest method to improve the segmentation performance is cascading several same or different U-Nets. The representation of cascaded U-Net architecture can be grossly divided into two groups, one is segmenting regions of interest firstly followed by target segmentation [10,11] and the other is rough pre-segmentation followed by explicit segmentation [12][13][14][15][16]. Christ et al [10] first applied cascaded U-Net for automatic liver and lesion segmentation, in which the predicted liver ROIs segmented by the former U-Net are fed into the latter one as inputs to segment lesions only.…”

Section: Introductionmentioning

confidence: 99%

“…Christ et al [10] first applied cascaded U-Net for automatic liver and lesion segmentation, in which the predicted liver ROIs segmented by the former U-Net are fed into the latter one as inputs to segment lesions only. Liu et al [11] proposed Cascaded Atrous Dual-Attention UNet to leverage the interslice context information and emphasize the salient features of tumors. 3D AU-Net is first employed to get the coarse localization of the liver, whose prediction results are then concatenated with the input 3D feature map to preserve the z-axis information, followed by 2D ADAU-Net for precise tumor segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Inner Cascaded U2-Net: An Improvement to Plain Cascaded U-Net

Wu¹,

Liu²,

Liang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Deep neural networks are now widely used in the medical image segmentation field for their performance superiority and no need of manual feature extraction. U-Net has been the baseline model since the very beginning due to a symmetrical U-structure for better feature extraction and fusing and suitable for small datasets. To enhance the segmentation performance of U-Net, cascaded U-Net proposes to put two U-Nets successively to segment targets from coarse to fine. However, the plain cascaded U-Net faces the problem of too less between connections so the contextual information learned by the former U-Net cannot be fully used by the latter one. In this article, we devise novel Inner Cascaded U-Net and Inner Cascaded U 2 -Net as improvements to plain cascaded U-Net for medical image segmentation. The proposed Inner Cascaded U-Net adds inner nested connections between two U-Nets to share more contextual information. To further boost segmentation performance, we propose Inner Cascaded U 2 -Net, which applies residual U-block to capture more global contextual information from different scales. The proposed models can be trained from scratch in an end-to-end fashion and have been evaluated on Multimodal Brain Tumor Segmentation Challenge (BraTS) 2013 and ISBI Liver Tumor Segmentation Challenge (LiTS) dataset in comparison to related U-Net, cascaded U-Net, U-Net++, U 2 -Net and state-of-the-art methods. Our experiments demonstrate that our proposed Inner Cascaded U-Net and Inner Cascaded U 2 -Net achieve better segmentation performance in terms of dice similarity coefficient and hausdorff distance as well as get finer outline segmentation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Inner Cascaded U2-Net: An Improvement to Plain Cascaded U-Net

Wu¹,

Liu²,

Liang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…Xiaocong Chen et al [16] proposed a novel U-Net architecture using aggregated residual blocks and a soft attention mechanism for segmentation of regions infected with COVID-19. Yu-Cheng Liu et al [17] proposed a cascaded atrous dual-attention U-Net for accurate tumor segmentation. They introduced a cascade structure to extend low-resolution quality prediction, and proposed a dual-attention module to improve the functional expression of tumor segmentation.…”

Section: Introductionmentioning

confidence: 99%

Improved U-Net with Residual Attention Block for Mixed-Defect Wafer Maps

Cha

Jeong

2022

Applied Sciences

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Detecting defect patterns in semiconductors is very important for discovering the fundamental causes of production defects. In particular, because mixed defects have become more likely with the development of technology, finding them has become more complex than can be performed by conventional wafer defect detection. In this paper, we propose an improved U-Net model using a residual attention block that combines an attention mechanism with a residual block to segment a mixed defect. By using the proposed method, we can extract an improved feature map by suppressing irrelevant features and paying attention to the defect to be found. Experimental results show that the proposed model outperforms those in the existing studies.

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“…However, they have two significant shortcomings. First, CNN's fail to encode global statistics of the input image, as shown by the benefits from utilizing attention [13,14] operations in computer vision problems. Second, did not examine the temporal dimension (flicker frequency) of flame.…”

Section: Introductionmentioning

confidence: 99%

Fire Detection using Transformer Network

Shahid

Hua

2021

Proceedings of the 2021 International Conference on Multimedia Retrieval

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Technological breakthroughs in computing have empowered visionbased surveillance systems to detect fire using transformers framework. Over the last few decades, convolutional neural networks (CNNs) have been broadly applied for many computer vision-related problems and provided satisfactory results. However, due to the inductive prejudices embedded in convolutional operations, it cannot comprehend long-range dependencies. Vision transformers (ViT) has recently become an alternative to CNN for a vision problem by factoring an image as a patches sequence and leverage intraattention between pixels. This paper shows that ViT is a viable tool for automated fire detection by aggregating features from the whole spatial context. The proposed method is tested on benchmark fire datasets to reveal the framework's strength and effectiveness. CCS CONCEPTS• Computing methodologies → Object identification.

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Cascaded atrous dual attention U-Net for tumor segmentation

Cited by 12 publications

References 50 publications

Inner Cascaded U2-Net: An Improvement to Plain Cascaded U-Net

Inner Cascaded U2-Net: An Improvement to Plain Cascaded U-Net

Improved U-Net with Residual Attention Block for Mixed-Defect Wafer Maps

Fire Detection using Transformer Network

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