This is the winning solution of the Endocv-2021 grand challange.

Last update: Dec 03, 2022

Related tags

Deep Learning divergent-nets

Overview

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.py

Pretrained checkpoint paths

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.sh

For 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.pth

Sample 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}
}

Contact us:

[email protected], [email protected], [email protected]

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This is the winning solution of the Endocv-2021 grand challange.

Related tags

Overview

Endocv2021-winner

[Paper]

Dependencies

Tri-Unet

Block diagram of Tri-Unet

How to train Tri-Unet and other basic models to DivergentNet?

Pretrained checkpoint paths

DivergentNet

Merging and predicting from divergent networks

Sample predictions from different models used in DivergentNets and it's own output.

Citation

Contact us:

[email protected], [email protected], [email protected]

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Releases(checkpoints_v1)

checkpoints_v1(Mar 19, 2021)

Owner

Vajira Thambawita

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