Adaptive Context Selection for Polyp Segmentation

Zhang, Ruifei; Li, Guanbin; Li, Zhen; Cui, Shuguang; Qian, Dahong; Yu, Yizhou

doi:10.1007/978-3-030-59725-2_25

Cited by 134 publications

(78 citation statements)

References 18 publications

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“…Models. We collect several open source models from the field of polyp segmentation, for a total of nine comparative models, including U-Net [4], UNet++ [22], PraNet [5], SFA [80], MSEG [33], ACSNet [47], DCRNet [36], EU-Net [50] and SANet [9]. For fair comparison, we use their open source codes to evaluate on the same training and testing sets.…”

Section: B Datasets and Compared Modelsmentioning

confidence: 99%

“…4, where our model shows a steady improvement on both ColonDB and ETIS. Endoscene [14] Model mDic mIoU F w β Sα mE ξ maxE ξ MAE U-Net [4] 0.710 0.627 0.684 0.843 0.847 0.875 0.022 UNet++ [22] 0.707 0.624 0.687 0.839 0.834 0.898 0.018 SFA [80] 0.467 0.329 0.341 0.640 0.644 0.817 0.065 MSEG [33] 0.874 0.804 0.852 0.924 0.948 0.957 0.009 ACSNet [47] 0.573 0.519 0.547 0.703 0.698 0.941 0.235 DCRNet [36] 0.856 0.788 0.830 0.921 0.943 0.960 0.010 PraNet [5] 0.871 0.797 0.843 0.925 0.950 0.972 0.010 EU-Net [50] 0.837 0.765 0.805 0.904 0.919 0.933 0.015 SANet [9] 0.888 0.815 0.859 0.928 0.962 0.972 0.008 Polyp-PVT (Ours) 0.900 0.833 0.884 0.935 0.973 0.981 0.007…”

Section: Quantitative Analysis Of Generalization Abilitymentioning

confidence: 99%

“…We use PVTv2 [64] as our baseline (Bas.) and evaluate module effectiveness by removing or replacing CVC-300-TV [88] Model mDic mIoU F w β Sα mE ξ maxE ξ MAE U-Net [4] 0.631 0.516 0.567 0.793 0.826 0.849 0.027 UNet++ [22] 0.638 0.527 0.581 0.796 0.831 0.847 0.024 ResUNet++ [23] 0.533 0.410 0.469 0.703 0.718 0.720 0.052 ACSNet [47] 0.732 0.627 0.703 0.837 0.871 0.875 0.016 PraNet [5] 0.716 0.624 0.700 0.833 0.852 0.904 0.016 PNS-Net [62] 0.813 0.710 0.778 0.909 0.921 0.942 0.013 Polyp-PVT (Ours) 0.880 0.802 0.869 0.915 0.961 0.965 0.011 components from the complete Polyp-PVT and comparing the variants with the standard version. The standard version is denoted as "Polyp-PVT (PVT+CFM+CIM+SAM)", where "CFM", "CIM" and "SAM" indicate the usage of the CFM, CIM and SAM, respectively.…”

Section: F Ablation Studymentioning

confidence: 99%

“…For fair comparison, we re-train our model with the same training datasets and use the same testing set as PNS-Net [62]. We compare our model on three standard benchmarks (i.e., CVC-300-TV [88], CVC-612-T [10], and CVC-612-V [10]) against six state-of-the-art approaches, including U-Net [4], UNet++ [22], ResUNet++ [23], ACSNet [47], PraNet [5], PNS-Net [62], in Tab. VI and Tab.…”

Section: G Video Polyp Segmentationmentioning

confidence: 99%

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Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

Dong¹,

Wang²,

Fan³

et al. 2021

Preprint

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Most polyp segmentation methods use CNNs as their backbone, leading to two key issues when exchanging information between the encoder and decoder: 1) taking into account the differences in contribution between different-level features; and 2) designing effective mechanism for fusing these features. Different from existing CNN-based methods, we adopt a transformer encoder, which learns more powerful and robust representations. In addition, considering the image acquisition influence and elusive properties of polyps, we introduce three novel modules, including a cascaded fusion module (CFM), a camouflage identification module (CIM), a and similarity aggregation module (SAM). Among these, the CFM is used to collect the semantic and location information of polyps from high-level features, while the CIM is applied to capture polyp information disguised in low-level features. With the help of the SAM, we extend the pixel features of the polyp area with high-level semantic position information to the entire polyp area, thereby effectively fusing cross-level features. The proposed model, named Polyp-PVT, effectively suppresses noises in the features and significantly improves their expressive capabilities. Extensive experiments on five widely adopted datasets show that the proposed model is more robust to various challenging situations (e.g., appearance changes, small objects) than existing methods, and achieves the new state-of-the-art performance. The proposed model is available at https://github.com/DengPingFan/Polyp-PVT.

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Section: B Datasets and Compared Modelsmentioning

confidence: 99%

Section: Quantitative Analysis Of Generalization Abilitymentioning

confidence: 99%

Section: F Ablation Studymentioning

confidence: 99%

Section: G Video Polyp Segmentationmentioning

confidence: 99%

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Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

Dong¹,

Wang²,

Fan³

et al. 2021

Preprint

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show abstract

“…Therefore, early diagnosis and treatment of polyps are of great significance. Fortunately, with the assistance of computer technology, a lot of automatic polyp segmentation models [1,4,5,6,13,15,17,22,23,25] have been developed and achieved remarkable progress.…”

Section: Introductionmentioning

confidence: 99%

Shallow Attention Network for Polyp Segmentation

Wei

Zhang

et al. 2021

Preprint

Self Cite

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Accurate polyp segmentation is of great importance for colorectal cancer diagnosis. However, even with a powerful deep neural network, there still exists three big challenges that impede the development of polyp segmentation. (i) Samples collected under different conditions show inconsistent colors, causing the feature distribution gap and overfitting issue; (ii) Due to repeated feature downsampling, small polyps are easily degraded; (iii) Foreground and background pixels are imbalanced, leading to a biased training. To address the above issues, we propose the Shallow Attention Network (SANet) for polyp segmentation. Specifically, to eliminate the effects of color, we design the color exchange operation to decouple the image contents and colors, and force the model to focus more on the target shape and structure. Furthermore, to enhance the segmentation quality of small polyps, we propose the shallow attention module to filter out the background noise of shallow features. Thanks to the high resolution of shallow features, small polyps can be preserved correctly. In addition, to ease the severe pixel imbalance for small polyps, we propose a probability correction strategy (PCS) during the inference phase. Note that even though PCS is not involved in the training phase, it can still work well on a biased model and consistently improve the segmentation performance. Quantitative and qualitative experimental results on five challenging benchmarks confirm that our proposed SANet outperforms previous state-of-the-art methods by a large margin and achieves a speed about 72FPS.

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UACENet: Uncertain area attention and cross‐image context extraction network for polyp segmentation

Wang

Gao

et al. 2023

Int J Imaging Syst Tech

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Accurately segmenting polyp from colonoscopy images is essential for early screening and diagnosis of colorectal cancer. In recent years, with the proposed encoder‐decoder architecture, many advanced methods have been applied to this task and have achieved significant improvements. However, accurate segmentation of polyps has always been a challenging task due to the irregular shape and size of polyps, the low contrast between the polyp and the background in some images, and the influence of the environment such as illumination and mucus. In order to tackle these challenges, we propose a novel uncertain area attention and cross‐image context extraction network for accurate polyp segmentation, which consists of the uncertain area attention module (UAAM), the cross‐image context extraction module (CCEM), and the adaptive fusion module (AFM). UAAM is guided by the output prediction of the adjacent decoding layer, and focuses on the difficult region of the boundary without neglecting the attention to the background and foreground so that more edge details and uncertain information can be captured. CCEM innovatively captures multi‐scale global context within an image and implicit contextual information between multiple images, fusing them to enhance the extraction of global location information. AFM fuses the local detail information extracted by UAAM and the global location information extracted by CCEM with the decoding layer feature for multiple fusion and adaptive attention to enhance feature representation. Our method is extensively experimented on four public datasets and generally achieves state‐of‐the‐art performance compared to other advanced methods.

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Adaptive Context Selection for Polyp Segmentation

Cited by 134 publications

References 18 publications

Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

Shallow Attention Network for Polyp Segmentation

UACENet: Uncertain area attention and cross‐image context extraction network for polyp segmentation

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