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Video Polyp Segmentation On Sun Seg Hard

Métriques

Dice

S-Measure

Sensitivity

mean E-measure

mean F-measure

weighted F-measure

Résultats

Résultats de performance de divers modèles sur ce benchmark

Tableau comparatif

Nom du modèle	Dice	S-Measure	Sensitivity	mean E-measure	mean F-measure	weighted F-measure
Modèle 1	0.708	0.783	0.618	0.787	0.684	0.636
progressively-normalized-self-attention	0.675	0.767	0.579	0.755	0.656	0.609
dynamic-context-sensitive-filtering-network	0.317	0.514	0.364	0.522	0.303	0.263
the-emergence-of-objectness-learning-zero	0.252	0.472	0.213	0.527	0.141	0.128
pranet-parallel-reverse-attention-network-for	0.598	0.717	0.512	0.735	0.607	0.544
unet-a-nested-u-net-architecture-for-medical	-	-	0.467	-	-	-
sali-short-term-alignment-and-long-term-1	0.822	0.874	0.830	0.920	0.822	0.790
matnet-motion-attentive-transition-network	0.712	0.785	0.579	0.755	0.645	0.578
see-more-know-more-unsupervised-video-object-1	0.606	0.670	0.380	0.627	0.506	0.443
autosam-adapting-sam-to-medical-images-by	0.759	0.822	0.726	0.866	0.764	0.714
Modèle 11	0.706	0.786	0.607	0.775	0.688	0.634
shallow-attention-network-for-polyp	0.598	0.706	0.505	0.743	0.580	0.526
video-polyp-segmentation-a-deep-learning	0.737	0.797	0.623	0.793	0.709	0.653
Modèle 14	0.584	0.682	0.415	0.660	0.510	0.443
lgrnet-local-global-reciprocal-network-for	0.865	-	-	-	-	-
self-prompting-polyp-segmentation-in	0.902	0.894	0.852	0.941	0.932	-
full-duplex-strategy-for-video-object	0.699	0.724	0.491	0.694	0.611	0.541
u-net-convolutional-networks-for-biomedical	-	-	0.429	-	-	-