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depth-peeling-reprojection

KidneyStainTranslation

This repo provides a framework to train CycleGAN- and U-GAT-IT-based translators for unsupervised stain-to-stain translation in histology.

Training

To train a CycleGAN translator (e.g. incorporating a prior segmentation model), use the following command:

python ./KidneyStainTranslation/train.py --stain aSMA --stainB PAS --dataroot <path-to-data> --resultsPath <path-to-store-results> --netD n_layers --netG unet_7 --ngf 32 --ndf 32 --batch_size 3 --niters_init 0 --lr 0.0001 --preprocess none --niters 300000 --load_size 640 --crop_size 640 --lambda_A 1 --lambda_B 1 --lambda_id 1 --niters_linDecay 100 --saveModelEachNIteration 10000 --validation_freq 1000 --n_layers_D 4 --gpu_ids 0 --update_TB_images_freq 5000 --use_segm_model --lambda_Seg 1

Testing

Use the same arguments to test the trained translator:

python ./KidneyStainTranslation/test.py --stain aSMA --stainB PAS --dataroot <path-to-data> --resultsPath <path-to-store-results> --netD n_layers --netG unet_7 --ngf 32 --ndf 32 --batch_size 3 --niters_init 0 --lr 0.0001 --preprocess none --niters 300000 --load_size 640 --crop_size 640 --lambda_A 1 --lambda_B 1 --lambda_id 1 --niters_linDecay 100 --saveModelEachNIteration 10000 --validation_freq 1000 --n_layers_D 4 --gpu_ids 0 --update_TB_images_freq 5000 --use_segm_model --lambda_Seg 1

Contact

Nassim Bouteldja
Institute of Pathology
RWTH Aachen University Hospital
Pauwelsstrasse 30
52074 Aachen, Germany
E-mail: nbouteldja@ukaachen.de

/**************************************************************************
*                                                                         *
*   Copyright (C) 2021 by RWTH Aachen University                          *
*   http://www.rwth-aachen.de                                             *
*                                                                         *
*   License:                                                              *
*                                                                         *
*   This software is dual-licensed under:                                 *
*   • Commercial license (please contact: lfb@lfb.rwth-aachen.de)         *
*   • AGPL (GNU Affero General Public License) open source license        *
*                                                                         *
***************************************************************************/