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算力平台

Medical Image Segmentation On Synapse Multi

评估指标

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评测结果

各个模型在此基准测试上的表现结果

Paper Title
Interactive AI-SAM gt box90.66-AI-SAM: Automatic and Interactive Segment Anything Model
Medical SAM Adapter89.80-Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
MedSegDiff-v289.50-MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer
nnUNet88.8010.78nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
MedNeXt-L (5x5x5)88.76-MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation
MIST86.9211.07MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder
nnFormer86.5710.63nnFormer: Interleaved Transformer for Volumetric Segmentation
AgileFormer86.1112.88AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation
MERIT84.9013.22Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation
Automatic AI-SAM84.21-AI-SAM: Automatic and Interactive Segment Anything Model
ParaTransCNN83.8615.86ParaTransCNN: Parallelized TransCNN Encoder for Medical Image Segmentation
EMCAD83.6315.68EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation
PAG-TransYnet83.4315.82Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging
SegFormer3D82.15-SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation
MISSFormer81.9618.20MISSFormer: An Effective Medical Image Segmentation Transformer
TransUNet81.19-S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging
SelfReg-UNet: SwinUNet80.54-SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation
SelfReg-UNet: Vanilla UNet80.34-SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation
FCB Former80.26-Adaptive t-vMF Dice Loss for Multi-class Medical Image Segmentation
SETR79.60-Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
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