Medical Image Segmentation On Cvc Colondb

mIoU

mean Dice

평가 결과

이 벤치마크에서 각 모델의 성능 결과

비교 표

모델 이름	mIoU	mean Dice
g-cascade-efficient-cascaded-graph	0.7460	0.8261
transfuse-fusing-transformers-and-cnns-for	0.676	0.744
emcad-efficient-multi-scale-convolutional	-	0.9231
uacanet-uncertainty-augmented-context	0.704	0.783
duat-dual-aggregation-transformer-network-for	0.737	0.819
sam-eg-segment-anything-model-with-egde	0.689	0.774
promise-promptable-medical-image-segmentation	0.789	0.874
comma-propagating-complementary-multi-level	0.689	0.754
kdas3-knowledge-distillation-via-attention	0.679	0.759
uacanet-uncertainty-augmented-context	0.678	0.751
metaformer-and-cnn-hybrid-model-for-polyp	0.9096	0.9526
meta-polyp-a-baseline-for-efficient-polyp	0.79	0.867
medical-image-segmentation-via-cascaded	0.7453	0.8254
hardnet-mseg-a-simple-encoder-decoder-polyp	0.660	0.731
transfuse-fusing-transformers-and-cnns-for	0.696	0.773
a-comprehensive-study-on-colorectal-polyp	0.8466	0.8474
esfpnet-efficient-deep-learning-architecture	0.730	0.811
stepwise-feature-fusion-local-guides-global	0.721	0.802
polyp-pvt-polyp-segmentation-with-pyramid	0.727	0.808
caranet-context-axial-reverse-attention	0.689	0.773
hardnet-dfus-an-enhanced-harmonically	-	0.774
using-duck-net-for-polyp-image-segmentation-1	0.8785	0.9353
pranet-parallel-reverse-attention-network-for	0.649	0.709