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Video Polyp Segmentation On Sun Seg Hard

Metrics

Dice

S-Measure

Sensitivity

mean E-measure

mean F-measure

weighted F-measure

Results

Performance results of various models on this benchmark

Model Name	Dice	S-Measure	Sensitivity	mean E-measure	mean F-measure	weighted F-measure	Paper Title	Repository
ACSNet	0.708	0.783	0.618	0.787	0.684	0.636	-	-
PNSNet	0.675	0.767	0.579	0.755	0.656	0.609	Progressively Normalized Self-Attention Network for Video Polyp Segmentation
DCF	0.317	0.514	0.364	0.522	0.303	0.263	Dynamic Context-Sensitive Filtering Network for Video Salient Object Detection	-
AMD	0.252	0.472	0.213	0.527	0.141	0.128	The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos
PraNet	0.598	0.717	0.512	0.735	0.607	0.544	PraNet: Parallel Reverse Attention Network for Polyp Segmentation
UNet++	-	-	0.467	-	-	-	UNet++: A Nested U-Net Architecture for Medical Image Segmentation
SALI	0.822	0.874	0.830	0.920	0.822	0.790	SALI: Short-term Alignment and Long-term Interaction Network for Colonoscopy Video Polyp Segmentation
MAT	0.712	0.785	0.579	0.755	0.645	0.578	MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object Segmentation
COSNet	0.606	0.670	0.380	0.627	0.506	0.443	See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks
AutoSAM	0.759	0.822	0.726	0.866	0.764	0.714	AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder	-
2/3D	0.706	0.786	0.607	0.775	0.688	0.634	-	-
SANet	0.598	0.706	0.505	0.743	0.580	0.526	Shallow Attention Network for Polyp Segmentation
PNS+	0.737	0.797	0.623	0.793	0.709	0.653	Video Polyp Segmentation: A Deep Learning Perspective
PCSA	0.584	0.682	0.415	0.660	0.510	0.443	-	-
LGRNet	0.865	-	-	-	-	-	LGRNet: Local-Global Reciprocal Network for Uterine Fibroid Segmentation in Ultrasound Videos	-
YOLO-SAM 2	0.902	0.894	0.852	0.941	0.932	-	Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model
FSNet	0.699	0.724	0.491	0.694	0.611	0.541	Full-Duplex Strategy for Video Object Segmentation
UNet	-	-	0.429	-	-	-	U-Net: Convolutional Networks for Biomedical Image Segmentation

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