HyperAI超神经

Video Polyp Segmentation On Sun Seg Easy

评估指标

Dice
S measure
Sensitivity
mean E-measure
mean F-measure
weighted F-measure

评测结果

各个模型在此基准测试上的表现结果

模型名称
Dice
S measure
Sensitivity
mean E-measure
mean F-measure
weighted F-measure
Paper TitleRepository
PNSNet0.6760.7670.5740.7440.6640.616Progressively Normalized Self-Attention Network for Video Polyp Segmentation
YOLO-SAM 20.900.983.793.893.8-Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model
FSNet0.7020.7250.4930.6950.6300.551Full-Duplex Strategy for Video Object Segmentation
ACSNet0.7130.7820.6010.7790.6880.642--
UNet--0.420---U-Net: Convolutional Networks for Biomedical Image Segmentation
LGRNet0.853-----LGRNet: Local-Global Reciprocal Network for Uterine Fibroid Segmentation in Ultrasound Videos-
DCF0.3250.5230.3400.5140.3120.270Dynamic Context-Sensitive Filtering Network for Video Salient Object Detection-
COSNet0.5960.6540.3590.6000.4960.431See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks
PNS+0.7560.8060.6300.7980.7300.676Video Polyp Segmentation: A Deep Learning Perspective
MAT0.7100.7700.5420.7370.6410.575MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object Segmentation
AutoSAM0.7530.8150.6720.8550.7740.716AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder-
SANet0.6490.7200.5210.7450.6340.566Shallow Attention Network for Polyp Segmentation
PCSA0.5920.6800.3980.6600.5190.451--
UNet++--0.457---UNet++: A Nested U-Net Architecture for Medical Image Segmentation
PraNet0.6210.7330.5240.7530.6320.572PraNet: Parallel Reverse Attention Network for Polyp Segmentation
2/3D0.7220.7860.6030.7770.7080.652--
AMD0.2660.4740.2220.5330.1460.133The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos
SALI0.8250.8700.8110.9200.8310.794SALI: Short-term Alignment and Long-term Interaction Network for Colonoscopy Video Polyp Segmentation
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