medical-9

Video Polyp Segmentation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in video-polyp-segmentation-9

Trend

Dataset

Best Model

Actions

SUN-SEG-Easy (Unseen)

SUN-SEG-Hard (Unseen)

Libraries

i

Use these libraries to find video-polyp-segmentation-9 models and implementations

GewelsJI/PNS-Net

4 papers 104

Datasets

STARE

SUN-SEG-Easy (Unseen)

SUN-SEG-Hard (Unseen)

PolypGen

Subtasks

No subtasks available.

Most implemented papers

U-Net: Convolutional Networks for Biomedical Image Segmentation

O. Ronneberger, T. Brox, P. Fischer•Sun May 17 2015

It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.

90626

Content

SUN-SEG-Hard

SUN-SEG-Easy

STARE

PaddlePaddle/PaddleSeg

2 papers 8,204

DengPingFan/PraNet

2 papers 397

frgfm/Holocron

2 papers 299

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Paper Graph

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

Zongwei Zhou, M. R. Siddiquee, Nima Tajbakhsh, Jianming Liang•Tue Jul 17 2018

This paper presents UNet++, a new, more powerful architecture for medical image segmentation where the encoder and decoder sub-networks are connected through a series of nested, dense skip pathways, and argues that the optimizer would deal with an easier learning task when the feature maps from the decoder and encoder networks are semantically similar.

7964 0

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PraNet: Parallel Reverse Attention Network for Polyp Segmentation

Jianbing Shen, Ling Shao, Deng-Ping Fan, H. Fu, Ge-Peng Ji, Geng Chen, Tao Zhou•Fri Jun 12 2020

Quantitative and qualitative evaluations on five challenging datasets across six metrics show that the PraNet improves the segmentation accuracy significantly, and presents a number of advantages in terms of generalizability, and real-time segmentation efficiency.

1600 0

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Video Polyp Segmentation: A Deep Learning Perspective

L. Gool, Deng-Ping Fan, Kai Zhao, H. Fu, Ge-Peng Ji, Guobao Xiao, Yu-Cheng Chou, Geng Chen•Sat Mar 26 2022

This work introduces 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 and designs a simple but efficient baseline, named PNS+, which achieves the best performance and real-time inference speed.

146 0

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Progressively Normalized Self-Attention Network for Video Polyp Segmentation

Ling Shao, Deng-Ping Fan, Debesh Jha, H. Fu, Ge-Peng Ji, Yu-Cheng Chou, Geng Chen•Mon May 17 2021

The novel PNS-Net (Progressively Normalized Self-attention Network), which can efficiently learn representations from polyp videos with real-time speed on a single RTX 2080 GPU and no post-processing, is proposed.

169 0

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Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?

Risab Biswas•Fri Aug 11 2023

This study will see how Polyp-SAM++, a text prompt-aided SAM, can better utilize a SAM using text prompting for robust and more precise polyp segmentation.

28 0

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See More, Know More: Unsupervised Video Object Segmentation With Co-Attention Siamese Networks

F. Porikli, Jianbing Shen, Ling Shao, Wenguan Wang, Xiankai Lu, Chao Ma•Fri May 31 2019

This work introduces a novel network, called as CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view and proposes a unified and end-to-end trainable framework where different co-att attention variants can be derived for mining the rich context within videos.

521 0

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MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object Segmentation

Ling Shao, Yi Zhou, Yazhou Yao, Tianfei Zhou, Shunzhou Wang, Jianwu Li•Sun Mar 08 2020

A novel end-to-end learning neural network, i.e., MATNet, for zero-shot video object segmentation (ZVOS), motivated by the human visual attention behavior, which leverages motion cues as a bottom-up signal to guide the perception of object appearance.

211 0

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Dynamic Context-Sensitive Filtering Network for Video Salient Object Detection

Huchuan Lu, Zhongxuan Luo, Miaohui Zhang, Jie Liu, Yifei Wang, Yongri Piao, S. Yao, Jingjing Li•Thu Sep 30 2021

The ability to capture inter-frame dynamics has been critical to the development of video salient object detection (VSOD). While many works have achieved great success in this field, a deeper insight into its dynamic nature should be developed. In this work, we aim to answer the following questions: How can a model adjust itself to dynamic variations as well as perceive fine differences in the real-world environment; How are the temporal dynamics well introduced into spatial information over time? To this end, we propose a dynamic context-sensitive filtering network (DCFNet) equipped with a dynamic context-sensitive filtering module (DCFM) and an effective bidirectional dynamic fusion strategy. The proposed DCFM sheds new light on dynamic filter generation by extracting location-related affinities between consecutive frames. Our bidirectional dynamic fusion strategy encourages the interaction of spatial and temporal information in a dynamic manner. Experimental results demonstrate that our proposed method can achieve state-of-the-art performance on most VSOD datasets while ensuring a real-time speed of 28 fps. The source code is publicly available at https://github.com/OIPLab-DUT/DCFNet.

112 0

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Shallow Attention Network for Polyp Segmentation

Shuguang Cui, Ruimao Zhang, Yiwen Hu, Zhen Li, JunChao Wei, S.Kevin Zhou•Sun Aug 01 2021

The Shallow Attention Network (SANet) is proposed, which 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, and the shallow attention module to filter out the background noise of shallow features.

365 0

Paper Graph

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