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Medical Image Segmentation On Automatic

评估指标

Avg DSC

评测结果

各个模型在此基准测试上的表现结果

Paper TitleRepository
FCT94.26Adaptive t-vMF Dice Loss for Multi-class Medical Image Segmentation
Interactive AI-SAM gt box93.89AI-SAM: Automatic and Interactive Segment Anything Model
FCT93.02The Fully Convolutional Transformer for Medical Image Segmentation
LHU-Net92.65LHU-Net: A Light Hybrid U-Net for Cost-Efficient, High-Performance Volumetric Medical Image Segmentation
MIST92.56MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder
MERIT92.32Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation
MERIT-GCASCADE92.23G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
EMCAD92.12EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation
nnFormer92.06nnFormer: Interleaved Transformer for Volumetric Segmentation
Automatic AI-SAM92.06AI-SAM: Automatic and Interactive Segment Anything Model
PVT-GCASCADE91.95G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
TransCASCADE91.63Medical Image Segmentation via Cascaded Attention Decoding-
PVT-CASCADE91.46Medical Image Segmentation via Cascaded Attention Decoding-
SegFormer3D90.96SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation
TransUNet90.4S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging
SwinUnet90.00Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
TransUNet89.71TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
MISSFormer87.9MISSFormer: An Effective Medical Image Segmentation Transformer
R50-ViT-CUP87.57TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
R50-AttnUNet86.75TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
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Medical Image Segmentation On Automatic | SOTA | HyperAI超神经