CCBANet: Cascading Context and Balancing Attention for Polyp Segmentation

Nguyen, Tan-Cong; Nguyen, Tien-Phat; Diep, Gia‐Han; Tran-Dinh, Anh-Huy; Nguyên, Tam; Tran, Minh–Triet

doi:10.1007/978-3-030-87193-2_60

Cited by 49 publications

(29 citation statements)

References 16 publications

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“…In order to prove the effectiveness of the GCLDNet method, we selected some SOTA networks for comparison, including FCN ( 35 ), U-Net ( 36 ), UNet++ ( 37 ), FPN ( 38 ), PSPNet ( 39 ), SegNet ( 40 ), LinkNet ( 41 ), DeepLabV3 ( 42 ), MultiResUNet ( 43 ), CCBANet ( 44 ), U2net ( 45 ), and UNet3+ ( 46 ). To ensure the fairness of comparison process, comparative methods also adopt the same preprocessing method as GCLDNet and the parameters are adjusted to the optimal state.…”

Section: Resultsmentioning

confidence: 99%

GCLDNet: Gastric cancer lesion detection network combining level feature aggregation and attention feature fusion

Wang

Liu

et al. 2022

Front. Oncol.

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BackgroundAnalysis of histopathological slices of gastric cancer is the gold standard for diagnosing gastric cancer, while manual identification is time-consuming and highly relies on the experience of pathologists. Artificial intelligence methods, particularly deep learning, can assist pathologists in finding cancerous tissues and realizing automated detection. However, due to the variety of shapes and sizes of gastric cancer lesions, as well as many interfering factors, GCHIs have a high level of complexity and difficulty in accurately finding the lesion region. Traditional deep learning methods cannot effectively extract discriminative features because of their simple decoding method so they cannot detect lesions accurately, and there is less research dedicated to detecting gastric cancer lesions.MethodsWe propose a gastric cancer lesion detection network (GCLDNet). At first, GCLDNet designs a level feature aggregation structure in decoder, which can effectively fuse deep and shallow features of GCHIs. Second, an attention feature fusion module is introduced to accurately locate the lesion area, which merges attention features of different scales and obtains rich discriminative information focusing on lesion. Finally, focal Tversky loss (FTL) is employed as a loss function to depress false-negative predictions and mine difficult samples.ResultsExperimental results on two GCHI datasets of SEED and BOT show that DSCs of the GCLDNet are 0.8265 and 0.8991, ACCs are 0.8827 and 0.8949, JIs are 0.7092 and 0.8182, and PREs are 0.7820 and 0.8763, respectively.ConclusionsExperimental results demonstrate the effectiveness of GCLDNet in the detection of gastric cancer lesions. Compared with other state-of-the-art (SOTA) detection methods, the GCLDNet obtains a more satisfactory performance. This research can provide good auxiliary support for pathologists in clinical diagnosis.

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Section: Resultsmentioning

confidence: 99%

GCLDNet: Gastric cancer lesion detection network combining level feature aggregation and attention feature fusion

Wang

Liu

et al. 2022

Front. Oncol.

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“…As the newly-proposed methods, SANet [41] and MSNet [42] design the shallow attention module and subtraction unit, respectively, to achieve precise and efficient segmentation. Additionally, several works opt for introducing additional constraints via three main-stream manners: exerting explicit boundary supervision [43][44][45][46][47] , introducing implicit boundary-aware representation [48][49][50] , and exploring uncertainty for ambiguous regions [51] . 2) Transformer-based approaches.…”

Section: Image Polyp Segmentation (Ips)mentioning

confidence: 99%

“…The dependency will be calculated between the anchor frame and the sampled consecutive frames within a sliding window. Following PraNet [48] , we use the same backbone, Res2Net-50 [65] , to extract the feature in the layer. To alleviate the computational burden, we adopt an RFB-like [66] module to reduce the channel dimension of the extracted feature and generate the anchor feature .…”

Section: Global Encodermentioning

confidence: 99%

“…We elaborately select eight typical video-based object/ polyp segmentation methods, including COSNet [69] , MAT [70] , PCSA [62] , 2/3D [2] , AMD [71] , DCF [72] , FSNet [73] , and PNSNet [4] . We also add five image-based object/ polyp segmentation methods to validate the effectiveness of per-frame prediction ability, including UNet [34] , UNet++ [35] , ACSNet [38] , PraNet [48] , and SANet [41] . For a fair comparison, all the competitors utilize the same training dataset as our PNS+ and reach convergence under their default settings.…”

Section: Competitorsmentioning

confidence: 99%

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Video Polyp Segmentation: A Deep Learning Perspective

Xiao

Chou

et al. 2022

Mach. Intell. Res.

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We present the first comprehensive video polyp segmentation (VPS) study in the deep learning era. Over the years, developments in VPS are not moving forward with ease due to the lack of a large-scale dataset with fine-grained segmentation annotations. To address this issue, we first introduce a high-quality frame-by-frame annotated VPS dataset, named SUN-SEG, which contains 158 690 colonoscopy video frames from the well-known SUN-database. We provide additional annotation covering diverse types, i.e., attribute, object mask, boundary, scribble, and polygon. Second, we design a simple but efficient baseline, named PNS+, which consists of a global encoder, a local encoder, and normalized self-attention (NS) blocks. The global and local encoders receive an anchor frame and multiple successive frames to extract long-term and short-term spatial-temporal representations, which are then progressively refined by two NS blocks. Extensive experiments show that PNS+ achieves the best performance and real-time inference speed (170 fps), making it a promising solution for the VPS task. Third, we extensively evaluate 13 representative polyp/object segmentation models on our SUN-SEG dataset and provide attribute-based comparisons. Finally, we discuss several open issues and suggest possible research directions for the VPS community. Our project and dataset are publicly available at https://github.com/GewelsJI/VPS.

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“…Nguyen et al, 2021] 99.02 70.68 79.77 95.82 83.68 88.31 ICGNet(Ours) 99.15 74.97 82.64 96.56 85.92 91.40…”

mentioning

confidence: 99%

ICGNet: Integration Context-based Reverse-Contour Guidance Network for Polyp Segmentation

Duan¹,

Chen²,

Zhou³

et al. 2022

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

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An adversary can fool deep neural network object detectors by generating adversarial noises. Most of the existing works focus on learning local visible noises in an adversarial "patch" fashion. However, the 2D patch attached to a 3D object tends to suffer from an inevitable reduction in attack performance as the viewpoint changes. To remedy this issue, this work proposes the Coated Adversarial Camouflage (CAC) to attack the detectors in arbitrary viewpoints. Unlike the patch trained in the 2D space, our camouflage generated by a conceptually different training framework consists of 3D rendering and dense proposals attack. Specifically, we make the camouflage perform 3D spatial transformations according to the pose changes of the object. Based on the multi-view rendering results, the top-n proposals of the region proposal network are fixed, and all the classifications in the fixed dense proposals are attacked simultaneously to output errors. In addition, we build a virtual 3D scene to fairly and reproducibly evaluate different attacks. Extensive experiments demonstrate the superiority of CAC over the existing attacks, and it shows impressive performance both in the virtual scene and the real world. This poses a potential threat to the security-critical computer vision systems.

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CCBANet: Cascading Context and Balancing Attention for Polyp Segmentation

Cited by 49 publications

References 16 publications

GCLDNet: Gastric cancer lesion detection network combining level feature aggregation and attention feature fusion

GCLDNet: Gastric cancer lesion detection network combining level feature aggregation and attention feature fusion

Video Polyp Segmentation: A Deep Learning Perspective

ICGNet: Integration Context-based Reverse-Contour Guidance Network for Polyp Segmentation

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