Residual-Attention UNet++: A Nested Residual-Attention U-Net for Medical Image Segmentation

Li, Zan; Zhang, Hong; Li, Zhengzhen; Ren, Zuyue

doi:10.3390/app12147149

Cited by 20 publications

(7 citation statements)

References 31 publications

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“…Table.2 shows the performance of proposed MA-UNet model with existing methods such as RDCT-U-Net [7], Residual-Attention UNet++ [10], and SDU-Net [14]. The proposed MA-UNet model achieved the better segmentation performance than other methods for liver and tumors segmentation.…”

Section: Comparative Analysismentioning

confidence: 95%

“…Residual-Attention UNet++, an expansion of the UNet++ model with a residual unit and attention mechanism for image segmentation, was proposed by Li et al [10]. First, the deterioration issue is improved by the residual unit.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Li et al [10] Residual-Attention UNet++ -improves the segmentation accuracy -the connection of model's get complicated.…”

Section: Table: 1 Summary Of Existing Liver Tumor Segmentation Authormentioning

confidence: 99%

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Multi-Scale Attention Based U-Net Model for Liver Tumor Segmentation

Jenisha

Dickson

2023

EPRA

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It is essential to automatically evaluate the position and size of the liver tumour for radiologists, diagnosis, and the clinical process. Many U-Net-based variants have been suggested in recent years to enhance the segmentation results for medical image segmentation, but they are unable to describe the global spatial and channel relationships among lesion regions. To overcome this issues, we proposed a novel network called Multi-scale Attention UNet (MA-UNet) to address this problem by adding a self-attention mechanism into our approach to adaptively combine local features with their global dependencies. The attention mechanism of the MA-UNet allows it to capture complex contextual dependencies. Position-wise Attention Block and Multi-scale Fusion Attention Block are the two blocks that we have developed. The feature interdependencies in spatial dimensions, which represent the spatial dependencies between pixels in a global view, are modelled using the Position-wise Attention Block. A multi-scale semantic feature fusion attention block is also used to capture the channel dependencies between any feature map. On the MICCAI 2017 LiTS Competition dataset, we assess our methodology. Compared to other cutting-edge methods, the suggested way performs better. The Dice and VOE values of liver tumors segmentation are 0.749 ± 0.08 and 0.21 ± 0.06 respectively. KEYWORDS: Liver tumor segmentation, Attention mechanism, Deep learning.

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Section: Comparative Analysismentioning

confidence: 95%

Section: Literature Reviewmentioning

confidence: 99%

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Multi-Scale Attention Based U-Net Model for Liver Tumor Segmentation

Jenisha

Dickson

2023

EPRA

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“…Experimental results show that despite many challenges of microscopic image analysis, the proposed model is a reliable system for the automatic diagnosis of anthrax and other tissue diseases. Li et al 36 proposed a residual-attention UNetþþ, which is an extension of the UNetþþ model with a residual unit and attention mechanism. The residual unit improves the degradation problem and the attention mechanism can increase the weight of the target area and suppress the background area irrelevant to the segmentation task.…”

Section: Related Workmentioning

confidence: 99%

“…Li et al. 36 proposed a residual-attention UNet++, which is an extension of the UNet++ model with a residual unit and attention mechanism. The residual unit improves the degradation problem and the attention mechanism can increase the weight of the target area and suppress the background area irrelevant to the segmentation task.…”

Section: Related Workmentioning

confidence: 99%

Pixel-level pruning deep supervision UNet++ for detecting fabric defects

Zhang,

Kang

2023

Textile Research Journal

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Due to the complexity of fabric texture and the diversity of defect types, fabric defect detection is quite challenging. At present, fabric defect detection algorithms based on deep learning have achieved good detection results, but there are still some key issues to be solved. First, the existing network models remain unchanged once they have been built. When they are used to detect defects of different kinds of fabrics, the network model cannot be adjusted flexibly according to the characteristics of the fabrics, which reduces the efficiency of algorithm detection. Second, the imbalanced category of fabric defect samples makes model training more challenging. Moreover, the number of defective pixels is very small compared with the number of pixels in the whole image, which further increases the difficulty of fabric defect detection methods. To solve these problems, we propose a pixel-level pruning end-to-end deep supervision DSUNet++ architecture for fabric defect detection. The DSUNet++ architecture consists of an encoder, a decoder, and a series of cascade operations for fusing the detailed features of the shallow layer and the abstract features of the deep layer. The deep supervision is embedded into the outputs of different depths in the DSUNet++, which can prune the network reasonably according to the characteristics of different kinds of fabrics, so as to balance the depth, speed and precision of the network. Furthermore, the cross-entropy loss function weighted by the median frequency CEloss_MFB is introduced to overcome the problem of imbalanced fabric defect sample categories and detection rate decrease of small pixel defects. The experimental results show that the average detection rate of the method is 97.68% and 99.01% in the defect detection of raw fabric and patterned fabric, respectively.

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Intelligent Automated Pancreas Segmentation Using U-Net Model Variants

Bakasa,

Viriri

2023

Computational Collective Intelligence

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Residual-Attention UNet++: A Nested Residual-Attention U-Net for Medical Image Segmentation

Cited by 20 publications

References 31 publications

Multi-Scale Attention Based U-Net Model for Liver Tumor Segmentation

Multi-Scale Attention Based U-Net Model for Liver Tumor Segmentation

Pixel-level pruning deep supervision UNet++ for detecting fabric defects

Intelligent Automated Pancreas Segmentation Using U-Net Model Variants

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