labeled-images-for-ulcerative-colitis

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labeled-images-for-ulcerative-colitis

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Codes to process and train LIMUC dataset

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# LIMUC (Labeled Images for Ulcerative Colitis) Dataset
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5827695.svg)](https://doi.org/10.5281/zenodo.5827695)

![Alt text](https://raw.githubusercontent.com/GorkemP/labeled-images-for-ulcerative-colitis/main/images/sample_images.png)

In this repository, you will find scripts to process and train the LIMUC dataset.

## How to use this repository?

1. Read the main [article](https://academic.oup.com/ibdjournal/advance-article-abstract/doi/10.1093/ibd/izac226/6830946?login=false) related to the LIMUC dataset to get how this dataset is curated, used, and what are the performance results.
2. Download the LIMUC dataset from [here](https://zenodo.org/record/5827695#.Yi8GJ3pByUk).
3. Unzip downloaded files.
4. Install conda virtual environment with `conda env create -f environment.yml` and activate the environment (see [here](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-from-an-environment-yml-file)).
5. Set up [wandb](https://wandb.ai/) platform on your machine (host OS).
6. Run the desired script in `dataset/` folder to create train-val-test sets for the target task (see below).
7. Run the training script for the target task.

##  Which script in the dataset folder should be run?

### 1. If you want to train your model on a single train-val set and get the performance on a test set:

1.1 After downloading the dataset, run `split_train_val_test.py` on `patient_based_classified_images` to create different sets split by patient-level.

```
python split_train_val_test.py 
--target_dir "path/to/target/folder where train, val, and test sets will be created" 
--published_folder_path "path/to/patient_based_classified_images" 
--test_set_ratio 0.15 
--val_set_ratio 0.15
```  
1.2 Run `train_classification_model.py` or `train_regression_model.py` indicating train and validation sets.

```
python train_classification_model.py 
--train_dir "path/to/train/folder" 
--val_dir "path/to/validation/folder"
```

1.3 After the training finishes, run `inference_classification_based_model.py` or `inference_regression_based_model.py`, indicating path to train/test sets and model weights (checkpoints).

```
python inference_classification_based_model.py 
--train_dir "path/to/train/folder" 
--test_dir "path/to/test/folder" 
--model_name="ResNet18"  
--checkpoint="weights/best_ResNet18.pth.tar"
```

### 2. If you want to train your model on the Cross-Validation setting as described in the LIMUC paper:

2.1 After downloading the dataset, run `generate_10_CV_folds_from_json_files.py`. This script will form the same folds as used in the LIMUC paper.
```
python generate_10_CV_folds_from_json_files.py
--json_folder "path/to/cross_validation_folds_train_val_info"
--train_val_folder "path/to/train_and_validation_sets"
--output_folder "path/to/cross validation folds"
```

2.2 Run `train_classification_model_CV.py` or `train_regression_model_CV.py`.
```
python train_classification_model_CV.py 
--CV_fold_path "path/to/CV_folds_folder" 
--test_set_path "path/to/test" 
--model_name ResNet18
```

### 3. If you want to train your model on a different CV setting (e.g., different test set ratio or # of CV folds):

3.1 After downloading the dataset, run `split_test_set_and_n_fold_rest.py` by indicating the test set ratio and # of CV folds.

The following code splits 20% of the images as a test set and creates 5-folds for CV from the rest 80% (each fold has 64% train, 16% val).
```
python split_test_set_and_n_fold_rest.py
--CV_folder_path "path/to/target/folder"
--published_folder_path "path/to/patient_based_classified_images"
--fold_num 5
--test_set_ratio 0.2
```

3.2 Run `train_classification_model_CV.py` or `train_regression_model_CV.py` (same as 2.2).

---
#### For citation please use the following BibTeX entries:
#### For the article

```BibTeX
@article{10.1093/ibd/izac226,
    author = {Polat, Gorkem and Kani, Haluk Tarik and Ergenc, Ilkay and Ozen Alahdab, Yesim and Temizel, Alptekin and Atug, Ozlen},
    title = "{Improving the Computer-Aided Estimation of Ulcerative Colitis Severity According to Mayo Endoscopic Score by Using Regression-Based Deep Learning}",
    journal = {Inflammatory Bowel Diseases},
    year = {2022},
    month = {11},
    abstract = "{ Assessment of endoscopic activity in ulcerative colitis (UC) is important for treatment decisions and monitoring disease progress. However, substantial inter- and intraobserver variability in grading impairs the assessment. Our aim was to develop a computer-aided diagnosis system using deep learning to reduce subjectivity and improve the reliability of the assessment.The cohort comprises 11 276 images from 564 patients who underwent colonoscopy for UC. We propose a regression-based deep learning approach for the endoscopic evaluation of UC according to the Mayo endoscopic score (MES). Five state-of-the-art convolutional neural network (CNN) architectures were used for the performance measurements and comparisons. Ten-fold cross-validation was used to train the models and objectively benchmark them. Model performances were assessed using quadratic weighted kappa and macro F1 scores for full Mayo score classification and kappa statistics and F1 score for remission classification.Five classification-based CNNs used in the study were in excellent agreement with the expert annotations for all Mayo subscores and remission classification according to the kappa statistics. When the proposed regression-based approach was used, (1) the performance of most of the models statistically significantly increased and (2) the same model trained on different cross-validation folds produced more robust results on the test set in terms of deviation between different folds.Comprehensive experimental evaluations show that commonly used classification-based CNN architectures have successful performance in evaluating endoscopic disease activity of UC. Integration of domain knowledge into these architectures further increases performance and robustness, accelerating their translation into clinical use.}",
    issn = {1078-0998},
    doi = {10.1093/ibd/izac226},
    url = {https://doi.org/10.1093/ibd/izac226},
    note = {izac226},
    eprint = {https://academic.oup.com/ibdjournal/advance-article-pdf/doi/10.1093/ibd/izac226/47071389/izac226.pdf},
}
```

```BibTeX
@inproceedings{polat2022class,
  title={Class distance weighted cross-entropy loss for ulcerative colitis severity estimation},
  author={Polat, Gorkem and Ergenc, Ilkay and Kani, Haluk Tarik and Alahdab, Yesim Ozen and Atug, Ozlen and Temizel, Alptekin},
  booktitle={Annual Conference on Medical Image Understanding and Analysis},
  pages={157--171},
  year={2022},
  organization={Springer}
}
```

#### For the dataset
```BibTeX
@dataset{gorkem_polat_2022_5827695,
  author       = {Gorkem Polat and
                  Haluk Tarik Kani and
                  Ilkay Ergenc and
                  Yesim Ozen Alahdab and
                  Alptekin Temizel and
                  Ozlen Atug},
  title        = {{Labeled Images for Ulcerative Colitis (LIMUC) 
                   Dataset}},
  month        = mar,
  year         = 2022,
  publisher    = {Zenodo},
  version      = 1,
  doi          = {10.5281/zenodo.5827695},
  url          = {https://doi.org/10.5281/zenodo.5827695}
}
```

## Important Notes

Since each patient has several images, when performing train-val-test splitting, it is highly
recommended performing it at patient-level because similar images may go in different sets, resulting in high val/test set performance.
Therefore, use the dataset splitting scripts in this repository.

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