CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects

Lou, Ange; Guan, Shuyue; Ko, Hanseok; Loew, Murray H.

doi:10.48550/arxiv.2108.07368

Cited by 13 publications

(17 citation statements)

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“…Dice mIoU SFFormer-L [25] 0.9357 0.8905 PraNet [34] 0.898 0.849 TransFuse-L [26] 0.918 0.868 CaraNet [35] 0.918 0.865 ResUNet++ [17] 0.8133 0.793 U-Net++ [37] 0.821 0.722 U-Net [14] 0.818 0.742 PRAPNet 0.942 0.906 Table 4. Evaluation results of different models using the CVC-ClinicDB datase.…”

Section: Methodsmentioning

confidence: 99%

“…The attention mechanism [24] focuses on a subset of its input and it is widely used in the application of natural language translations. In recent years, it has been used for semantic segmentation tasks [35,36] such as pixel prediction. The attention mechanism helps neural networks to determine which parts of the network need more attention.…”

Section: Attention Unitsmentioning

confidence: 99%

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PRAPNet: A Parallel Residual Atrous Pyramid Network for Polyp Segmentation

Han

et al. 2022

Sensors

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In a colonoscopy, accurate computer-aided polyp detection and segmentation can help endoscopists to remove abnormal tissue. This reduces the chance of polyps developing into cancer, which is of great importance. In this paper, we propose a neural network (parallel residual atrous pyramid network or PRAPNet) based on a parallel residual atrous pyramid module for the segmentation of intestinal polyp detection. We made full use of the global contextual information of the different regions by the proposed parallel residual atrous pyramid module. The experimental results showed that our proposed global prior module could effectively achieve better segmentation results in the intestinal polyp segmentation task compared with the previously published results. The mean intersection over union and dice coefficient of the model in the Kvasir-SEG dataset were 90.4% and 94.2%, respectively. The experimental results outperformed the scores achieved by the seven classical segmentation network models (U-Net, U-Net++, ResUNet++, praNet, CaraNet, SFFormer-L, TransFuse-L).

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Section: Methodsmentioning

confidence: 99%

Section: Attention Unitsmentioning

confidence: 99%

PRAPNet: A Parallel Residual Atrous Pyramid Network for Polyp Segmentation

Han

et al. 2022

Sensors

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“…Inspired by CaraNet [37], we use Channel-wise Feature Pyramid (CFP) to extract features from the encoder in multiscale views. As depicted in Fig.…”

Section: Refinement Modulementioning

confidence: 99%

“…CaraNet [37] also enhanced the Reverse Attention module by an axial attention block, which is a straightforward generalization of self-attention that naturally aligns with the multiple dimensions of the tensors. This module is supposed to filter the necessary information for the refinement process.…”

Section: Refinement Modulementioning

confidence: 99%

ColonFormer: An Efficient Transformer Based Method for Colon Polyp Segmentation

Duc

Nguyen

et al. 2022

IEEE Access

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Identifying polyps is challenging for automatic analysis of endoscopic images in computeraided clinical support systems. Models based on convolutional networks (CNN), transformers, and their combinations have been proposed to segment polyps with promising results. However, those approaches have limitations either in modeling the local appearance of the polyps only or lack of multi-level feature representation for spatial dependency in the decoding process. This paper proposes a novel network, namely ColonFormer, to address these limitations. ColonFormer is an encoder-decoder architecture capable of modeling long-range semantic information at both encoder and decoder branches. The encoder is a lightweight architecture based on transformers for modeling global semantic relations at multi scales. The decoder is a hierarchical network structure designed for learning multi-level features to enrich feature representation. Besides, a refinement module is added with a new skip connection technique to refine the boundary of polyp objects in the global map for accurate segmentation. Extensive experiments have been conducted on five popular benchmark datasets for polyp segmentation, including Kvasir, CVC-Clinic DB, CVC-ColonDB, CVC-T, and ETIS-Larib. Experimental results show that our ColonFormer outperforms other state-of-the-art methods on all benchmark datasets.

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“…Many image segmentation models based on the Convolutional Neural Networks (CNN) recently achieved excellent learning ability in several polyp segmentation benchmarks. [5,1,2,10,4] However, due to the top-down modelling method of the CNN model and the variability in the morphology of polyps but relatively simple structure of the polyps image, this model lacks generalisation ability and is difficult to to process unseen ⋆ Contributed Equally datasets. To improve the generalisation ability of the deep learning model, we shall incorporate the Transformer architecture into the polyp segmentation task.…”

Section: Introductionmentioning

confidence: 99%

Stepwise Feature Fusion: Local Guides Global

Wang¹,

Huang²,

Tang³

et al. 2022

Preprint

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Colonoscopy, currently the most efficient and recognized colon polyp detection technology, is necessary for early screening and prevention of colorectal cancer. However, due to the varying size and complex morphological features of colonic polyps as well as the indistinct boundary between polyps and mucosa, accurate segmentation of polyps is still challenging. Deep learning has become popular for accurate polyp segmentation tasks with excellent results. However, due to the structure of polyps image and the varying shapes of polyps, it easy for existing deep learning models to overfitting the current dataset. As a result, the model may not process unseen colonoscopy data. To address this, we propose a new State-Of-The-Art model for medical image segmentation, the SSFormer, which uses a pyramid Transformer encoder to improve the generalization ability of models. Specifically, our proposed Progressive Locality Decoder can be adapted to the pyramid Transformer backbone to emphasize local features and restrict attention dispersion. The SSFormer achieves statet-of-the-art performance in both learning and generalization assessment.

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CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects

Cited by 13 publications

References 0 publications

PRAPNet: A Parallel Residual Atrous Pyramid Network for Polyp Segmentation

PRAPNet: A Parallel Residual Atrous Pyramid Network for Polyp Segmentation

ColonFormer: An Efficient Transformer Based Method for Colon Polyp Segmentation

Stepwise Feature Fusion: Local Guides Global

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