EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation

Benchmark Model Rank Results
medical-image-segmentation-on-2018-dataEMCAD#2Dice: 0.9274
medical-image-segmentation-on-acdcEMCAD#4Dice Score: 0.9212
medical-image-segmentation-on-automaticEMCAD#8Avg DSC: 92.12
medical-image-segmentation-on-bkai-ighEMCAD#2Average Dice: 0.9296
medical-image-segmentation-on-cvc-clinicdbEMCAD#3mean Dice: 0.9521
medical-image-segmentation-on-cvc-colondbEMCAD#3mean Dice: 0.9231
medical-image-segmentation-on-emEMCAD#2DSC: 95.53
medical-image-segmentation-on-etisEMCAD#3mean Dice: 0.9229
medical-image-segmentation-on-isic-2018-1EMCAD#3DSC: 90.96
medical-image-segmentation-on-isic2018EMCAD#3mean Dice: 0.9096
medical-image-segmentation-on-kvasir-segEMCAD#12mean Dice: 0.928
medical-image-segmentation-on-miccai-2015-1EMCAD#4Avg DSC: 83.63Avg HD: 15.68
medical-image-segmentation-on-synapse-multiEMCAD#12Avg DSC: 83.63Avg HD: 15.68