This project consists of a collection of CNN-based and Transformer-based models for comparison experiments in layer segmentation programs.
- Incorporating cross-species homologous data for collaborative training can enhance the performance of models in segmenting the cortex and medulla in human kidney histopathology images.
- To train CNN-based models, including
UNet
,PSPNet
, andDeeplab-v3+
:
CUDA_VISIBLE_DEVICES=0 python train.py
To validate and get IoU
and Dice score
, change the path of weight and run:
python predict_img.py
python get_metrics.py
- To train Transformer-based models, including
TransUNet
andSwin-UNet
:
CUDA_VISIBLE_DEVICES=0 python train.py --root_path '' --num_classes 5 --img_size 1024
To validate and get IoU
and Dice score
, change the path of weight and run:
python predict_img.py
python get_metrics.py
By utilizing external homologous data, the models have become better at perceiving edge textures, performing better in more precise localization of kidney layer boundaries.