We proposed a LSUnetMix modle which contains 3 modules: CIT (Channel Information Transmission), MDC (Multiscale Dilated Convolution), and PE (Prospect Enhancement).
Install from the requirement.txt
using:
pip install -r requirements.txt
The original data can be downloaded in following links:
- MoNuSeG Dataset - Link (Original)
- GLAS Dataset - Link (Original)
- DRIVE Dataset - Link (Original)
Then prepare the datasets in the following format for easy use of the code:
├── datasets
├── DRIVE
│ ├── Val_Folder
│ │ ├── img
│ │ └── labelcol
│ └── Train_Folder
│ ├── img
│ └── labelcol
├── GlaS
│ ├── Val_Folder
│ │ ├── img
│ │ └── labelcol
│ └── Train_Folder
│ ├── img
│ └── labelcol
└── MoNuSeg
├── Val_Folder
│ ├── img
│ └── labelcol
└── Train_Folder
├── img
└── labelcol
First, change the settings in Config.py
, all the configurations including learning rate, batch size and etc. are in it.
Run:
python train_model.py
First, change the model_path and set the relative dataset in Config.py
.
Then run:
python test_model.py
You can get the Dice and IoU scores and the visualization results.
We provide the best models in in our paper, and here is the links.
- MoNuSeG model - Link (Original)
- GLAS model - Link (Original)
- GDRIVE model - Link (Original)