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MLLA-UNet

The codes for the work "MLLA-UNet: Mamba-like Linear Attention in an Efficient U-Shape Model for Medical Image Segmentation".

1. Pretrained Models: You can choose a pretrained model based on your preference.

model Resolution #Params FLOPs acc@1 config pretrained weights
MLLA-T 224 25M 4.2G 83.5 config TsinghuaCloud
MLLA-S 224 43M 7.3G 84.4 config TsinghuaCloud
MLLA-B 224 96M 16.2G 85.3 config TsinghuaCloud

Ref: [MLLA Official Implementation]

2. Prepare data

Preprocessing:

Ref: nnUNet

3. Environment

  • Please prepare an environment with python=3.9 and then use the command pip install -r requirements.txt for the dependencies.

4. Train/Test

  • Run the train script on synapse dataset. The batch size we used is 48. If you do not have enough GPU memory, the bacth size can be reduced to 12 or 6 to save memory.

  • Train

sh train.sh
  • Test
sh test.sh

References