Endocv2021-winner
[Paper]
This is the winning solution of the Endocv-2021 grand challange.
Dependencies
pytorch # tested with 1.7 and 1.8
torchvision
tqdm
pandas
numpy
albumentations # for augmentations
torchsummary
segmentation_models_pytorch # for basic segmentaion models
pyra_pytorch # pyra_pytorch.PYRADatasetFromDF is used. But this can be replaced with normal pytorch dataset.Tri-Unet
Block diagram of Tri-Unet
How to train Tri-Unet and other basic models to DivergentNet?
# To train Tri-unet
python tri_unet.py train \
--num_epochs 2 \
--device_id 0 \
--train_CSVs sample_CSV_files/C1.csv sample_CSV_files/C1.csv \
--val_CSVs sample_CSV_files/C2.csv sample_CSV_files/C3.csv \
--test_CSVs sample_CSV_files/C3.csv \
--out_dir ../temp_data \
--tensorboard_dir ../temp_data
# To train other models, you have to replace tri_unet.py with one of the follwings:
unet_plusplus.py
deeplabv3.py
deeplabv3_plusplus.pyPretrained checkpoint paths
- https://github.com/vlbthambawita/divergent-net/releases/download/checkpoints_v1/best_checkpoint_Deeplabv3.pth
- https://github.com/vlbthambawita/divergent-net/releases/download/checkpoints_v1/best_checkpoint_Depplabv3_plusplus.pth
- https://github.com/vlbthambawita/divergent-net/releases/download/checkpoints_v1/best_checkpoint_FPN.pth
- https://github.com/vlbthambawita/divergent-net/releases/download/checkpoints_v1/best_checkpoint_TriUnet.pth
- https://github.com/vlbthambawita/divergent-net/releases/download/checkpoints_v1/best_checkpoint_unet_plusplus.pth
DivergentNet
Merging and predicting from divergent networks
Set following parameters in inference_from_divergentNets.sh
--input_dir <directory to input images>
--output_dir <directory to save predicted mask>
--chk_paths <path to pretrained checkpoints. You can provide single checkpoint path or multiple checkpoint paths. Use a space to seperate multiple checkpoint paths or '\' as the given example paths.>Then run it:
bash inference_from_divergentNets.shFor Windows users we provide an inference script that supports windows. You can run it without the bash. For example:
python inference_from_divergentNets.py --input_dir C:\Users\xxx\GitHub\divergent-nets\input --output_dir C:\Users\xxx\GitHub\divergent-nets\output --chk_paths C:\Users\xxx\OneDrive\Dokumente\GitHub\divergent-nets\checkpoints\best_checkpoint_Deeplabv3.pthSample predictions from different models used in DivergentNets and it's own output.
Citation
@inproceedings{divergentNets,
title={DivergentNets: Medical Image Segmentation by Network Ensemble},
author={Thambawita, Vajira and Hicks, Steven A. and Halvorsen, P{\aa}l and Riegler, Michael A.},
booktitle={Proceedings of the 3rd International Workshop and Challenge on Computer Vision in Endoscopy (EndoCV 2021)
co-located with with the 17th IEEE International Symposium on Biomedical Imaging (ISBI 2021)},
year={2021}
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent