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Video Polyp Segmentation On Sun Seg Easy

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
progressively-normalized-self-attention	0.676	0.767	0.574	0.744	0.664	0.616
self-prompting-polyp-segmentation-in	0.90	0.9	83.7	93.8	93.8	-
full-duplex-strategy-for-video-object	0.702	0.725	0.493	0.695	0.630	0.551
Modèle 4	0.713	0.782	0.601	0.779	0.688	0.642
u-net-convolutional-networks-for-biomedical	-	-	0.420	-	-	-
lgrnet-local-global-reciprocal-network-for	0.853	-	-	-	-	-
dynamic-context-sensitive-filtering-network	0.325	0.523	0.340	0.514	0.312	0.270
see-more-know-more-unsupervised-video-object-1	0.596	0.654	0.359	0.600	0.496	0.431
video-polyp-segmentation-a-deep-learning	0.756	0.806	0.630	0.798	0.730	0.676
matnet-motion-attentive-transition-network	0.710	0.770	0.542	0.737	0.641	0.575
autosam-adapting-sam-to-medical-images-by	0.753	0.815	0.672	0.855	0.774	0.716
shallow-attention-network-for-polyp	0.649	0.720	0.521	0.745	0.634	0.566
Modèle 13	0.592	0.680	0.398	0.660	0.519	0.451
unet-a-nested-u-net-architecture-for-medical	-	-	0.457	-	-	-
pranet-parallel-reverse-attention-network-for	0.621	0.733	0.524	0.753	0.632	0.572
Modèle 16	0.722	0.786	0.603	0.777	0.708	0.652
the-emergence-of-objectness-learning-zero	0.266	0.474	0.222	0.533	0.146	0.133
sali-short-term-alignment-and-long-term-1	0.825	0.870	0.811	0.920	0.831	0.794