Video Polyp Segmentation On Sun Seg Easy
평가 지표
Dice
S measure
Sensitivity
mean E-measure
mean F-measure
weighted F-measure
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
| 모델 이름 | 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 |
| 모델 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 |
| 모델 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 |
| 모델 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 |