Using DUCK-Net for Polyp Image Segmentation | ✓ Link | 0.9502 | | | | 0.9051 | | | 0.9628 | 0.9379 | DUCK-Net | 2023-11-03 |
EffiSegNet: Gastrointestinal Polyp Segmentation through a Pre-Trained EfficientNet-based Network with a Simplified Decoder | ✓ Link | 0.9488 | | | | 0.9065 | | 0.9513 | 0.9713 | 0.9321 | EffiSegNet-B5 | 2024-07-23 |
EffiSegNet: Gastrointestinal Polyp Segmentation through a Pre-Trained EfficientNet-based Network with a Simplified Decoder | ✓ Link | 0.9483 | | | | 0.9056 | | 0.9552 | 0.9679 | 0.9429 | EffiSegNet-B4 | 2024-07-23 |
From Semantic Segmentation of Natural Images to Medical Image Segmentation Using ViT-Based Architectures | | 0.947 | | | | 0.899 | | | | | SegMed | 2025-01-31 |
Adaptive t-vMF Dice Loss for Multi-class Medical Image Segmentation | ✓ Link | 0.9445 | | | | 0.8974 | | | | | FCB Former | 2022-07-16 |
FCB-SwinV2 Transformer for Polyp Segmentation | | 0.9420 | | | | 0.8973 | | | | | FCB-SwinV2 Transformer | 2023-02-02 |
Spatially Exclusive Pasting: A General Data Augmentation for the Polyp Segmentation | | 0.9411 | | | | 0.9002 | | | | | SEP | 2022-11-15 |
LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation | ✓ Link | 0.9409 | | | | 0.8912 | | | 0.8964 | 0.9038 | LM-Net | 2025-01-07 |
MetaFormer and CNN Hybrid Model for Polyp Image Segmentation | ✓ Link | 0.939 | | | | 0.885 | | | | | RAPUNet | 2024-09-16 |
FCN-Transformer Feature Fusion for Polyp Segmentation | ✓ Link | 0.9385 | | | | 0.8903 | | | | | FCBFormer | 2022-08-17 |
HarDNet-DFUS: An Enhanced Harmonically-Connected Network for Diabetic Foot Ulcer Image Segmentation and Colonoscopy Polyp Segmentation | ✓ Link | 0.9363 | | | | 0.8894 | | | | | HarDNet-DFUS | 2022-09-15 |
Stepwise Feature Fusion: Local Guides Global | ✓ Link | 0.9357 | | | | 0.8905 | | | | | SSFormer-L | 2022-03-07 |
S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging | ✓ Link | 0.932 | | | | | | | | | FCBFormer | 2024-12-17 |
ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video | ✓ Link | 0.931 | | | | 0.887 | | | | | ESFPNet-L | 2022-07-15 |
UGCANet: A Unified Global Context-Aware Transformer-based Network with Feature Alignment for Endoscopic Image Analysis | | 0.928 | | | | 0.881 | | | | | UGCANet | 2023-07-12 |
EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation | ✓ Link | 0.928 | | | | | | | | | EMCAD | 2024-05-11 |
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation | ✓ Link | 0.9274 | | | | 0.8790 | | | | | PVT-GCASCADE | 2023-10-24 |
ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentation | ✓ Link | 0.927 | | | | 0.877 | | | | | ColonFormer | 2022-05-17 |
RaBiT: An Efficient Transformer using Bidirectional Feature Pyramid Network with Reverse Attention for Colon Polyp Segmentation | ✓ Link | 0.927 | | | | 0.873 | | | | | RaBiT | 2023-07-12 |
GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentation | ✓ Link | 0.9263 | | | | 0.8843 | | | | | GMSRF-Net | 2021-11-20 |
Medical Image Segmentation via Cascaded Attention Decoding | ✓ Link | 0.9258 | | | | 0.8776 | | | | | PVT-CASCADE | 2023-01-03 |
DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation | ✓ Link | 0.924 | 0.023 | | | 0.876 | | | | | DuAT | 2022-12-21 |
MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation | ✓ Link | 0.9217 | | | | 0.8914 | | | | | MSRF-Net | 2021-05-16 |
Adaptation of Distinct Semantics for Uncertain Areas in Polyp Segmentation | ✓ Link | 0.92 | | | | 0.871 | | | | | ADSNet | 2024-05-13 |
CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects | ✓ Link | 0.918 | 0.023 | 0.929 | 0.968 | 0.865 | | | | | CaraNet | 2021-08-16 |
TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation | ✓ Link | 0.918 | | | | 0.868 | | | | | TransFuse-L | 2021-02-16 |
TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation | ✓ Link | 0.918 | | | | 0.868 | | | | | TransFuse-S | 2021-02-16 |
BDG-Net: Boundary Distribution Guided Network for Accurate Polyp Segmentation | ✓ Link | 0.915 | 0.021 | 0.923 | 0.972 | 0.865 | | | | | BDG-Net | 2022-01-03 |
SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation | | 0.915 | | | | 0.862 | | | | | SAM-EG | 2024-06-21 |
UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection | ✓ Link | 0.915 | | | | 0.857 | | | | | UniNet | 2025-02-28 |
MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation | ✓ Link | 0.913 | 0.025 | | | 0.863 | | | | | MEGANet(Res2Net-50) | 2023-09-06 |
KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp Segmentation | ✓ Link | 0.913 | 0.027 | | | 0.848 | | | | | KDAS | 2023-12-13 |
HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS | ✓ Link | 0.912 | 0.025 | 0.923 | 0.958 | 0.857 | 116 | | | | HarDNet-MSEG | 2021-01-18 |
UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation | ✓ Link | 0.912 | 0.025 | 0.917 | 0.958 | 0.862 | | | | | UACANet-L | 2021-07-06 |
MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation | ✓ Link | 0.911 | 0.026 | | | 0.859 | | | | | MEGANet(ResNet-34) | 2023-09-06 |
ProMISe: Promptable Medical Image Segmentation using SAM | ✓ Link | 0.911 | | | | 0.851 | | | | | ProMISe | 2024-03-07 |
A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution | | 0.9085 | | | | 0.8615 | | | | | A-DenseUNet | 2021-02-19 |
UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation | ✓ Link | 0.905 | 0.026 | 0.914 | 0.951 | 0.852 | | | | | UACANet-S | 2021-07-06 |
COMMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation | | 0.904 | 0.024 | 0.925 | 0.963 | 0.860 | | | | | COMMA (ResNet-50) | 2022-02-17 |
Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation? | ✓ Link | 0.902 | | | | 0.862 | | 0.92 | | | Polyp-SAM++ | 2023-08-12 |
AG-CUResNeSt: A Novel Method for Colon Polyp Segmentation | ✓ Link | 0.902 | | | | 0.845 | | | | | AG-CUResNeSt | 2021-05-02 |
COMMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation | | 0.901 | 0.027 | 0.919 | 0.951 | 0.852 | | | | | COMMA (Res2Net-50) | 2022-02-17 |
TGANet: Text-guided attention for improved polyp segmentation | ✓ Link | 0.8982 | | | | 0.8330 | | | | | TGA-Net | 2022-05-09 |
PraNet: Parallel Reverse Attention Network for Polyp Segmentation | ✓ Link | 0.898 | 0.030 | 0.915 | 0.948 | 0.849 | | | | | PraNet | 2020-06-13 |
TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation | ✓ Link | 0.8884 | | | | 0.8214 | 48.61 | | | | TransResU-Net | 2022-06-17 |
Multi Kernel Positional Embedding ConvNeXt for Polyp Segmentation | ✓ Link | 0.8818 | | | | 0.8163 | | | | | PEFNet | 2023-01-17 |
FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation | ✓ Link | 0.8803 | 0.8153 | | | | | | | | FANet | 2021-03-31 |
TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing | ✓ Link | 0.8706 | | | | 0.8016 | 54.60 | | | | TransNetR | 2023-03-13 |
Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model | ✓ Link | 0.866 | | | | 0.764 | | | | | Yolo-SAM 2 | 2024-09-14 |
DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation | ✓ Link | 0.8576 | | | | 0.7800 | 69.59 | | | | DDANet | 2020-12-30 |
Dual Cross-Attention for Medical Image Segmentation | ✓ Link | 0.8516 | | | | 0.7434 | | | | | DoubleUnet-DCA | 2023-03-30 |
A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation | ✓ Link | 0.8508 | | | | 0.7800 | 69.59 | | | | ResUNet++ + TTA + CRF | 2021-07-26 |
UNet++: A Nested U-Net Architecture for Medical Image Segmentation | ✓ Link | 0.8210 | 0.048 | 0.862 | 0.910 | | | | | | U-Net++ | 2018-07-18 |
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning | ✓ Link | 0.8206 | | | | 0.7239 | 182.38 | | | | ColonSegNet | 2020-11-15 |
U-Net: Convolutional Networks for Biomedical Image Segmentation | ✓ Link | 0.8180 | 0.055 | 0.858 | 0.893 | | | | | | U-Net | 2015-05-18 |
ResUNet++: An Advanced Architecture for Medical Image Segmentation | ✓ Link | 0.8133 | | | | | | | | | ResUNet++ | 2019-11-16 |
Kvasir-SEG: A Segmented Polyp Dataset | ✓ Link | 0.7877 | | | | | | | | | ResUNet | 2019-11-16 |
RUPNet: Residual upsampling network for real-time polyp segmentation | | 0.7658 | | | | 0.6553 | 152.60 | | | | RUPNet | 2023-01-06 |