Polyp Segmentation
7 papers with code • 2 benchmarks • 3 datasets
The goal of the project is to develop a computer-aided detection and diagnosis system for automatic polyp segmentation and detection.
Benchmarks
These leaderboards are used to track progress in Polyp Segmentation
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Kvasir-SEG
Kvasir
PolypGen
Most implemented papers
ResUNet++: An Advanced Architecture for Medical Image Segmentation
Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer.
BDG-Net: Boundary Distribution Guided Network for Accurate Polyp Segmentation
However, due to the different sizes of polyps and the unclear boundary between polyps and their surrounding mucosa, it is challenging to segment polyps accurately.
TGANet: Text-guided attention for improved polyp segmentation
Even though there are deep learning methods developed for this task, variability in polyp size can impact model training, thereby limiting it to the size attribute of the majority of samples in the training dataset that may provide sub-optimal results to differently sized polyps.
PatchRefineNet: Improving Binary Segmentation by Incorporating Signals from Optimal Patch-wise Binarization
Given the logit scores produced by the base segmentation model, each pixel is given a pseudo-label that is obtained by optimally thresholding the logit scores in each image patch.
Medical Image Segmentation via Cascaded Attention Decoding
SLDGroup/CASCADE
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Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023
To address this issue, we propose a novel attention-based decoder, namely CASCaded Attention DEcoder (CASCADE), which leverages the multiscale features of hierarchical vision transformers.
Multi Kernel Positional Embedding ConvNeXt for Polyp Segmentation
Specifically, with the increase in cases, the diagnosis and identification need to be faster and more accurate for many patients; in endoscopic images, the segmentation task has been vital to helping the doctor identify the position of the polyps or the ache in the system correctly.
TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing
Therefore, we intend to develop a novel real-time deep learning based architecture, Transformer based Residual network (TransNetR), for colon polyp segmentation and evaluate its diagnostic performance.
