A polyp image segmentation algorithm based on derivative networks is proposed because of some problems such as virtualization, holes, rough segmentation, and imperfect edge generation of traditional UNet. An improved ...
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A polyp is one of the major causes of gastroenterology, which leads to colorectal cancer. The detection of polyps by colonoscopy imaging is a significant challenge because of the diversity in polyp structure and lack ...
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A polyp is one of the major causes of gastroenterology, which leads to colorectal cancer. The detection of polyps by colonoscopy imaging is a significant challenge because of the diversity in polyp structure and lack of examination accuracy. To solve this problem, the automatic segmentation of polyps can be used to enhance examination accuracy and reduce gastrointestinal (GI) disease. In this paper, the framework of polyp image segmentation is developed by a deep learning approach, especially a convolutional neural network. This proposed framework used the Kvasir-SEG database, which contains 1000 GI polypimages and corresponding segmentation masks according to annotation by medical experts. This database is divided into 900 for training images and 100 for testing images. This framework is based on image preprocessing and two types of SegNet architecture to obtain the segmented polypimage. This paper has demonstrated state-of-the-art performance on both VGG-16, and VGG-19 networks for training and testing data to address colorectal cancer screening rates. The results confirmed that the VGG-19 model has outperformed the VGG-16 model via all evaluation parameters except sensitivity for the polypsegmentation on the Kvasir-SEG dataset. Additionally, it will support gastroenterologists during medical strategy to correctly choose the treatment with less time.
Fully-supervised polypsegmentation has accomplished significant triumphs over the years in advancing the early diagnosis of colorectal cancer. However, label-efficient solutions from weak supervision like scribbles a...
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ISBN:
(纸本)9783031439063;9783031439070
Fully-supervised polypsegmentation has accomplished significant triumphs over the years in advancing the early diagnosis of colorectal cancer. However, label-efficient solutions from weak supervision like scribbles are rarely explored yet primarily meaningful and demanding in medical practice due to the expensiveness and scarcity of densely-annotated polyp data. Besides, various deployment issues, including data shifts and corruption, put forward further requests for model generalization and robustness. To address these concerns, we design a framework of Spatial-Spectral Dual-branch Mutual Teaching and Entropy-guided Pseudo Label Ensemble Learning (S2ME). Concretely, for the first time in weakly-supervised medical imagesegmentation, we promote the dual-branch co-teaching framework by leveraging the intrinsic complementarity of features extracted from the spatial and spectral domains and encouraging cross-space consistency through collaborative optimization. Furthermore, to produce reliable mixed pseudo labels, which enhance the effectiveness of ensemble learning, we introduce a novel adaptive pixelwise fusion technique based on the entropy guidance from the spatial and spectral branches. Our strategy efficiently mitigates the deleterious effects of uncertainty and noise present in pseudo labels and surpasses previous alternatives in terms of efficacy. Ultimately, we formulate a holistic optimization objective to learn from the hybrid supervision of scribbles and pseudo labels. Extensive experiments and evaluation on four public datasets demonstrate the superiority of our method regarding in-distribution accuracy, out-of-distribution generalization, and robustness, highlighting its promising clinical significance. Our code is available at https://***/lofrienger/S2ME.
Colorectal carcinoma is a prevalent malignancy worldwide. Accurate polypsegmentation, along with endoscopic resection, can significantly reduce its incidence and mortality. Most polypsegmentation neural networks are...
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Colorectal carcinoma is a prevalent malignancy worldwide. Accurate polypsegmentation, along with endoscopic resection, can significantly reduce its incidence and mortality. Most polypsegmentation neural networks are CNN-based and single decoder strategy architectures, which learn limited robust representations. In this paper, we propose a novel network with the vision transformer and dual decoder refinement strategy called PVT2DNet to overcome some limitations of current networks and achieve more precise automated polypsegmentation. The PVT2DNet adopts a pyramid vision transformer encoder and enhances the multi-level features with the contextenhanced module (CEM). Moreover, instead of directly feeding features into a single decoder, we introduce a dual partial cascaded decoder refinement strategy to excavate more informative polyp cues. Extensive experimentations on five widely adopted datasets demonstrate the proposed network outperforms other state-of-the-art on most metrics.
Colonoscopy is one of the most direct and effective methods for detecting colon polyps;they are crucial for early screening and prevention of colorectal cancer (CRC). Accurate segmentation of polypimages is significa...
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Colonoscopy is one of the most direct and effective methods for detecting colon polyps;they are crucial for early screening and prevention of colorectal cancer (CRC). Accurate segmentation of polypimages is significant for the clinical management and treatment of CRC. However, polyp image segmentation is a challenging task because polyps vary in size, shape, and color, and have low contrast with the surrounding tissue and mucosa. To address these challenges, we propose a novel network called BGNet for polypsegmentation. BGNet consists of three modules: a boundary feature extraction module (BFEM), a mutual optimization module (MOM), and a convolutional attention module (CBAM). The BFEM extracts boundary features and predicts boundary maps, which complement and guide region features to accurately predict polyp regions and help BGNet generate precise prediction masks. The MOM optimizes the extracted features to enhance fine-grained feature representation. The CBAM performs attention operations on the feature map to focus on informative regions and suppress irrelevant information. We conduct quantitative and qualitative evaluations on five benchmark datasets, and the results show that BGNet outperforms other methods and has strong learning ability and generalization ability.
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