MIPC-Net

MIPC-Net: A Mutual Inclusion Mechanism for Precise Boundary Segmentation in Medical Images (https://arxiv.org/abs/2404.08201)

1.Prepare pre-trained ViT models

Pre-training
Model parameters

2.Prepare data

Please use the preprocessed data for research purposes.

3.Environment

Please prepare an environment with python=3.7(conda create -n envir python=3.7.12), 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 can be reduced to 12 or 16 to save memory(please also decrease the base_lr linearly), and both can reach similar performance.

CUDA_VISIBLE_DEVICES=0 python train.py --dataset Synapse --vit_name R50-ViT-B_16

Run the test script on synapse dataset. It supports testing for both 2D images and 3D volumes.

python test.py --dataset Synapse --vit_name R50-ViT-B_16

Reference

Google ViT
ViT-pytorch
segmentation_models.pytorch
TransUnet