DEEP LEARNING BASED MEDICAL IMAGE SEGMENTATION

This is a reproduction of "Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space".

Usage

• data file: put file in ./data/, write your detail link --- training set, validation set, testing set --- into input_pattern, val_input_pattern and test_input_pattern. or by modifying input of load_dataset
• Training and result display:
  • Run project1.ipynb
  • If want check loss curve, run loss_display.py
• The model download from release should be move into ./results/model for testing.

Dependencies

• pip install -r requirements.txt

File Description

• results:
  • ./results/Loss contains loss information for each epoch in txt format which can be read by loss_display
  • ./results/model contains the final model after domain generalization and data augmentation.
  • ./results/DG_result contains the DG result when the phase spectrum is used.

• project1.ipynb contains results of domain generalization, the feature space representation, training, validation and testing diagram.
• dataset.py this file mainly contains load_dataset function to load images with typical dataset format. Function domain generalization and data augmentation based on transformer is implemented within this file which could be applied by changing input of load_dataset.
• dis_rep.py contains distance metric like 2-norm, 1-norm, CS-distance SNR, P-SNR, SSIM, intra-clustering distance, inter-clustering distance.
• Result_disp.py the training result is saved in this file and then be plotted in histogram.
• loss_display.py used to plot loss curve.
• pro1.py is an old training python (not valid now).
• K_fold_validation.py is the K fold validation used to save weights and train the model.
• DG distance.py: file used to generate the distance between generalized image from 3 domain and the source image (not valid now)
• test.py: only for testing (not valid now)

Training Result

• See more: project1.ipynb;
• You can download our pre-trained model at https://github.com/QianrenLi/ad_sig_pro1/releases/tag/v1.

	Verification DICE	Test1 DICE	Test2 DICE	Test3 DICE	Test1 HD95	Test2 HD95	Test3 HD95
Cross Entropy	0.172	0.080	0.183	0.147	22.198	4.070	7.992
DICE1	0.404	0.115	0.363	0.289	11.689	2.596	4.136
DICE2	0.873	0.654	0.869	0.780	3.846	2.402	4.131
DICE+CE	0.893	0.654	0.899	0.805	13.055	1.948	4.140
DA+DICE+CE	0.910	0.733	0.913	0.865	4.166	1.534	2.486
DG+DICE+CE	0.901	0.715	0.907	0.849	3.707	1.618	2.821
DG+DA+DICE+CE	0.907	0.737	0.909	0.889	3.723	1.626	1.892