HRENet: A Hard Region Enhancement Network for Polyp Segmentation

Shen, Yutian; Xiao, Jia; Meng, Max Q.-H.

doi:10.1007/978-3-030-87193-2_53

Cited by 23 publications

(12 citation statements)

References 21 publications

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“…Some researchers even considered multiple parallel branches for robust features, such as those from the decoder [44] or intermediate stages [45,46]. Boundaries are often adopted as constraints explicitly [47][48][49] or implicitly [30,[50][51][52].…”

Section: Polyp Segmentation In Imagesmentioning

confidence: 99%

Parallel matters: Efficient polyp segmentation with parallel structured feature augmentation modules

Guo

Fang

Wang

et al. 2023

IET Image Processing

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The large variations of polyp sizes and shapes and the close resemblances of polyps to their surroundings call for features with long-range information in rich scales and strong discrimination. This article proposes two parallel structured modules for building those features. One is the Transformer Inception module (TI) which applies Transformers with different reception fields in parallel to input features and thus enriches them with more long-range information in more scales. The other is the Local-Detail Augmentation module (LDA) which applies the spatial and channel attentions in parallel to each block and thus locally augments the features from two complementary dimensions for more object details. Integrating TI and LDA, a new Transformer encoder based framework, Parallel-Enhanced Network (PENet), is proposed, where LDA is specifically adopted twice in a coarse-tofine way for accurate prediction. PENet is efficient in segmenting polyps with different sizes and shapes without the interference from the background tissues. Experimental comparisons with state-of-the-arts methods show its merits.

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Section: Polyp Segmentation In Imagesmentioning

confidence: 99%

Parallel matters: Efficient polyp segmentation with parallel structured feature augmentation modules

Guo

Fang

Wang

et al. 2023

IET Image Processing

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“…PraNet 12 leveraged a parallel partial decoder to obtain the global feature maps and a set of recurrent reverse attention modules to establish the relationships between regions and boundaries to achieve the most advanced performance of polyp segmentation. HRENet 34 developed an informative context enhancement module to explore and intensify the features from the lower‐level encoder with explicit attention on hard regions and an adaptive feature aggregation to select and aggregate the features from multiple semantic levels. An edge and structure consistency aware loss is proposed to train the HRENet to further boost the polyp segmentation performance.…”

Section: Related Workmentioning

confidence: 99%

Dual‐branch feature extraction network combined with Transformer and CNN for polyp segmentation

Liu,

Lin,

Han

et al. 2023

Int J Imaging Syst Tech

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To overcome the difficulty of accurate polyp segmentation, a novel encoder–decoder network DFETC‐Net is proposed, in which two encoders based on Swin Transformer and CNN are utilized to extract the global and local features respectively. Further, a new self‐attention and convolution feature fusion module is designed to fuse the two branch features to enhance the feature representative capability and alleviate the influence of the semantic gap. In the bottleneck, a new multi‐feature pyramid pooling module fuses all deep features from two branches to obtain multi‐scale information and promote segmentation accuracy. The coordinate attention is used in the skip connections between two shallow CNN blocks and corresponding decoder blocks to pay more attention to doubtful and complicated regions. Extensive experiments demonstrate the proposed network outperforms several state‐of‐the‐art methods in terms of both qualitative effects and quantitative measurements. All codes are available on https://github.com/LYJieH/DFETC-NET.

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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%

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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HRENet: A Hard Region Enhancement Network for Polyp Segmentation

Cited by 23 publications

References 21 publications

Parallel matters: Efficient polyp segmentation with parallel structured feature augmentation modules

Parallel matters: Efficient polyp segmentation with parallel structured feature augmentation modules

Dual‐branch feature extraction network combined with Transformer and CNN for polyp segmentation

Video Polyp Segmentation: A Deep Learning Perspective

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