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computer-vision-7

Polyp Segmentation

3260 papers • 126 benchmarks • 313 datasets

The goal of the project is to develop a computer-aided detection and diagnosis system for automatic polyp segmentation and detection.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in polyp-segmentation-7

Trend

Dataset

Best Model

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Kvasir-SEG

PolypGen

Libraries

Use these libraries to find polyp-segmentation-7 models and implementations

Datasets

Kvasir-SEG

Kvasir

PolypGen

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Most implemented papers

ResUNet++: An Advanced Architecture for Medical Image Segmentation

Debesh Jha, P. Smedsrud, M. Riegler, P. Halvorsen, Thomas de Lange, Dag Johansen, H. Johansen•Fri Nov 15 2019

ResUNet++ is proposed, which is an improved ResUNet architecture for colonoscopic image segmentation, which significantly outperforms U-Net and Res UNet, two key state-of-the-art deep learning architectures, by achieving high evaluation scores.

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Introduction Benchmarks Datasets Subtasks Libraries Papers

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TGANet: Text-guided attention for improved polyp segmentation

Debesh Jha, Sharib Ali, Nikhil Kumar Tomar, Ulas Bagci•Sun May 08 2022

This work introduces an auxiliary classification task to weight the text-based embedding that allows network to learn additional feature representations that can distinctly adapt to differently sized polyps and can adapt to cases with multiple polyps.

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BDG-Net: boundary distribution guided network for accurate polyp segmentation

Zihuan Qiu, Zhichuan Wang, Miaomiao Zhang, Ziyong Xu, Jie Fan, Linfeng Xu•Sun Jan 02 2022

A Boundary Distribution Guided Network (BDG-Net) is designed for accurate polyp segmentation and outperforms state-of-the-art models remarkably and maintains low computational complexity.

81 0

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PatchRefineNet: Improving Binary Segmentation by Incorporating Signals from Optimal Patch-wise Binarization

Daniel Kifer, S. Nagendra•Fri Nov 11 2022

PatchRefineNet (PRN), a small network that sits on top of a base segmentation model and learns to correct its patch-specific biases and reduce false positives/negatives, is proposed.

8 0

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Medical Image Segmentation via Cascaded Attention Decoding

Md Mostafijur Rahman, R. Marculescu•Sat Dec 31 2022

Transformers have shown great promise in medical image segmentation due to their ability to capture long-range dependencies through self-attention. However, they lack the ability to learn the local (contextual) relations among pixels. Previous works try to overcome this problem by embedding convolutional layers either in the encoder or decoder modules of transformers thus ending up sometimes with inconsistent features. To address this issue, we propose a novel attention-based decoder, namely CASCaded Attention DEcoder (CASCADE), which leverages the multi-scale features of hierarchical vision transformers. CASCADE consists of i) an attention gate which fuses features with skip connections and ii) a convolutional attention module that enhances the long-range and local context by suppressing background information. We use a multi-stage feature and loss aggregation framework due to their faster convergence and better performance. Our experiments demonstrate that transformers with CASCADE significantly outperform state-of-the-art CNN- and transformer-based approaches, obtaining up to 5.07% and 6.16% improvements in DICE and mIoU scores, respectively. CASCADE opens new ways of designing better attention-based decoders.

271 0

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Multi Kernel Positional Embedding ConvNeXt for Polyp Segmentation

Hai-Dang Nguyen, M. Tran, Trong-Hieu Nguyen Mau, Quoc-Huy Trinh, Nhat-Tan Bui•Mon Dec 19 2022

A novel framework composed of ConvNeXt backbone and Multi Kernel Positional Embedding block is proposed that can attain better accuracy and generalization in the polyps segmen-tation task.

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TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing

Debesh Jha, Nikhil Kumar Tomar, Ulas Bagci, Vanshali Sharma•Sun Mar 12 2023

This work intends to develop a novel real-time deep learning based architecture, Transformer based Residual network (TransNetR), for colon polyp segmentation and evaluate its diagnostic performance and explores the generalizability of the TransNetR by testing the proposed algorithm on the out-of-distribution dataset.

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Polyp Segmentation | State-of-the-Art