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Joint work with [Jiannan Chen](https://github.com/GJiananChen) 3 | 4 | ![b72196df50cf72595078f11b4a182c5](https://github.com/JunMa11/MICCAI-Reproducibility-Checklist/assets/19947331/af295a52-2651-46fd-a49c-bd882555d63e) 5 | 6 | 7 | **REPRODUCIBILITY 1: WHAT DOES IT NEED FOR A MICCAI PAPER TO BE REPRODUCIBLE?** 8 | 9 | 1. Create a machine learning reproducibility checklist specific to MICCAI papers. 10 | 2. Propose a machine learning code completeness checklist specific to MICCAI papers. 11 | 3. How to ensure reproducibility when the data cannot be shared? 12 | 13 | # 1. Reproducibility checklist for machine learning-based MICCAI papers 14 | 15 | The checklist builds on the [machine learning reproducibility checklist](https://www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist.pdf), but is specific to MICCAI papers. 16 | We also got lots of insights from 17 | 18 | - Mongan, J., Moy, L., & Kahn Jr, C. E. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A guide for authors and reviewers. Radiology: Artificial Intelligence, 2:2 (2020). 19 | - Norgeot, B., Quer, G., Beaulieu-Jones, B.K. et al. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nature Medicine 26, 1320–1324 (2020). 20 | - Reproducibility Criteria in [ELNMP 2020](https://2020.emnlp.org/call-for-papers) 21 | 22 | ## Methods 23 | 24 | - A clear description of the mathematical setting, algorithm, and/or model. 25 | - Network architecture details 26 | - layer details in each block/module 27 | - the number of parameters 28 | - Explanation of evaluation metrics (with links to code) 29 | 30 | ## Experiments 31 | 32 | - Dataset 33 | - for public dataset, providing data sources, e.g, references and URL links 34 | - for private dataset, providing the data acquisition source and characteristics (e.g., device, contrast agent...), the eligibility description, the ground truth standard (e.g., qualifications and preparation of annotators), annotation tools, analysis of inter-rater variability 35 | - details of train / validation / test splits 36 | - Preprocessing steps 37 | - cropping strategy 38 | - resampling method for anisotropic data 39 | - intensity normalization method 40 | - registration method for multi-sequence/modality data 41 | - Training protocols 42 | - computing infrastructure (e.g., GPU name, number, memory) 43 | - patch size and patch sampling strategy 44 | - batch size 45 | - optimizer, learning rate and its decay schedule 46 | - loss function 47 | - data augmentation methods 48 | - stopping criteria, and optimal model selection criteria 49 | - training time 50 | - Testing steps 51 | - if using patch-based strategy, describing the patch aggregation method 52 | - inference time 53 | - Postprocessing steps 54 | - Ablation study 55 | 56 | ## Results 57 | 58 | - Quantitative analysis of cross validation results and/or testing set results 59 | - average and standard deviation of evaluation metrics 60 | - statistical analysis (e.g., statistical methods, significant levels...) 61 | 62 | - Qualitative analysis 63 | - box/violin plot, ROC curves 64 | - visualized examples of both successful and **failed** cases 65 | 66 | 67 | 68 | 69 | # 2. Code checklist for machine learning-based MICCAI papers 70 | >A template README.md for code accompanying a Machine Learning-based MICCAI paper, which is built on [paperswithcode/releasing-research-code](https://github.com/paperswithcode/releasing-research-code). 71 | > 72 | >Dataset, preprocessing, and posting processing sections are added because these parts are very important to reproduce the results in medical image analysis community. 73 | 74 | This repository is the official implementation of [My Paper Title](TBA). 75 | 76 | >Optional: include a graphic explaining your approach/main result, bibtex entry, and link to demos, blog posts and tutorials 77 | 78 | ## Environments and Requirements 79 | 80 | - Windows/Ubuntu version 81 | - CPU, RAM, GPU information 82 | - CUDA version 83 | - python version 84 | 85 | To install requirements: 86 | 87 | ```setup 88 | pip install -r requirements.txt 89 | ``` 90 | 91 | >Describe how to set up the environment, e.g. pip/conda/docker commands, download datasets, etc... 92 | 93 | 94 | 95 | ## Dataset 96 | 97 | - A link to download the data (if publicly available) 98 | - A description of how to prepare the data (e.g., folder structures) 99 | 100 | ## Preprocessing 101 | 102 | A brief description of the preprocessing method 103 | 104 | - cropping 105 | - intensity normalization 106 | - resampling 107 | 108 | Running the data preprocessing code: 109 | 110 | ```python 111 | python preprocessing.py --input_path --output_path 112 | ``` 113 | 114 | ## Training 115 | 116 | 1. To train the model(s) in the paper, run this command: 117 | 118 | ```bash 119 | python train.py --input-data --alpha 10 --beta 20 120 | ``` 121 | 122 | >Describe how to train the models, with example commands, including the full training procedure and appropriate hyper-parameters. 123 | 124 | You can download trained models here: 125 | 126 | - [My awesome model](https://drive.google.com/mymodel.pth) trained on the above dataset with the above code. 127 | 128 | >Give a link to where/how the trained models can be downloaded. 129 | 130 | 131 | 2. To fine-tune the model on a customized dataset, run this command: 132 | 133 | ```bash 134 | python finetune.py --input-data --pre_trained_model_path --other_flags 135 | ``` 136 | 137 | 3. [Colab](https://colab.research.google.com/) jupyter notebook 138 | 139 | 140 | ## Inference 141 | 142 | 1. To infer the testing cases, run this command: 143 | 144 | ```python 145 | python inference.py --input-data --model_path --output_path 146 | ``` 147 | 148 | > Describe how to infer testing cases with the trained models. 149 | 150 | 2. [Colab](https://colab.research.google.com/) jupyter notebook 151 | 152 | 3. Docker containers on [DockerHub](https://hub.docker.com/) 153 | 154 | ```bash 155 | docker container run --gpus "device=0" -m 28G --name algorithm --rm -v $PWD/CellSeg_Test/:/workspace/inputs/ -v $PWD/algorithm_results/:/workspace/outputs/ algorithm:latest /bin/bash -c "sh predict.sh" 156 | ``` 157 | 158 | ## Evaluation 159 | 160 | To compute the evaluation metrics, run: 161 | 162 | ```eval 163 | python eval.py --seg_data --gt_data 164 | ``` 165 | 166 | >Describe how to evaluate the inference results and obtain the reported results in the paper. 167 | 168 | 169 | 170 | ## Results 171 | 172 | Our method achieves the following performance on [Brain Tumor Segmentation (BraTS) Challenge](https://www.med.upenn.edu/cbica/brats2020/) 173 | 174 | | Model name | DICE | 95% Hausdorff Distance | 175 | | ---------------- | :----: | :--------------------: | 176 | | My awesome model | 90.68% | 32.71 | 177 | 178 | >Include a table of results from your paper, and link back to the leaderboard for clarity and context. If your main result is a figure, include that figure and link to the command or notebook to reproduce it. 179 | 180 | 181 | ## Contributing 182 | 183 | >Pick a license and describe how to contribute to your code repository. 184 | 185 | ## Acknowledgement 186 | 187 | > We thank the contributors of public datasets. 188 | 189 | 190 | # 3. How to ensure reproducibility when the data cannot be shared? 191 | 192 | - Try to find a related public dataset to evaluate your method, e.g., [The Cancer Imaging Archive](https://www.cancerimagingarchive.net/), [grand-challenge](https://grand-challenge.org/challenges/). If none of the public datasets can be used to evaluate your method, please explicitly claim it, which is very helpful for communities to create task-driven public datasets. 193 | - Create a docker container to pack and share the proposed method. Many MICCAI challenges have used the docker as the submission, e.g., [ADAM](http://adam.isi.uu.nl/methods/submit/), [M&Ms](https://www.ub.edu/mnms/) 194 | 195 | -------------------------------------------------------------------------------- /templates/MICCAI-Code-Checklist.md: -------------------------------------------------------------------------------- 1 | >A template README.md for code accompanying a machine learning-based MICCAI paper, which is built on [paperswithcode/releasing-research-code](https://github.com/paperswithcode/releasing-research-code). 2 | > 3 | >Dataset, preprocessing, posting processing sections are added because these parts are very important to reproduce the results in medical image analysis community. 4 | 5 | # My Paper Title 6 | 7 | This repository is the official implementation of [My Paper Title](TBA). 8 | 9 | >Optional: include a graphic explaining your approach/main result, bibtex entry, link to demos, blog posts and tutorials 10 | 11 | ## Environments and Requirements 12 | 13 | - Windows/Ubuntu version 14 | - CPU, RAM, GPU information 15 | - CUDA version 16 | - python version 17 | 18 | To install requirements: 19 | 20 | ```setup 21 | pip install -r requirements.txt 22 | ``` 23 | 24 | >Describe how to set up the environment, e.g. pip/conda/docker commands, download datasets, etc... 25 | 26 | 27 | 28 | ## Dataset 29 | 30 | - A link to download the data (if publicly available) 31 | - A description about how to prepare the data (e.g., folder structures) 32 | 33 | ## Preprocessing 34 | 35 | A brief description of preprocessing method 36 | 37 | - cropping 38 | - intensity normalization 39 | - resampling 40 | 41 | Running the data preprocessing code: 42 | 43 | ```python 44 | python preprocessing.py --input_path --output_path 45 | ``` 46 | 47 | ## Training 48 | 49 | To train the model(s) in the paper, run this command: 50 | 51 | ```train 52 | python train.py --input-data --alpha 10 --beta 20 53 | ``` 54 | 55 | >Describe how to train the models, with example commands, including the full training procedure and appropriate hyper-parameters. 56 | 57 | 58 | 59 | ## Trained Models 60 | 61 | You can download trained models here: 62 | 63 | - [My awesome model](https://drive.google.com/mymodel.pth) trained on the above dataset with the above code. 64 | 65 | >Give a link to where/how the trained models can be downloaded. 66 | 67 | 68 | 69 | ## Inference 70 | 71 | To infer the testing cases, run this command: 72 | 73 | ```python 74 | python inference.py --input-data --model_path --output_path 75 | ``` 76 | 77 | > Describe how to infer on testing cases with the trained models. 78 | 79 | 80 | 81 | ## Evaluation 82 | 83 | To compute the evaluation metrics, run: 84 | 85 | ```eval 86 | python eval.py --seg_data --gt_data 87 | ``` 88 | 89 | >Describe how to evaluate the inference results and obtain the reported results in the paper. 90 | 91 | 92 | 93 | ## Results 94 | 95 | Our method achieves the following performance on [Brain Tumor Segmentation (BraTS) Challenge](https://www.med.upenn.edu/cbica/brats2020/) 96 | 97 | | Model name | DICE | 95% Hausdorff Distance | 98 | | ---------------- | :----: | :--------------------: | 99 | | My awesome model | 90.68% | 32.71 | 100 | 101 | >Include a table of results from your paper, and link back to the leaderboard for clarity and context. If your main result is a figure, include that figure and link to the command or notebook to reproduce it. 102 | 103 | 104 | ## Contributing 105 | 106 | >Pick a licence and describe how to contribute to your code repository. 107 | 108 | ## Acknowledgement 109 | 110 | > We thank the contributors of public datasets. 111 | -------------------------------------------------------------------------------- /templates/MICCAI-Paper-Checklist.md: -------------------------------------------------------------------------------- 1 | # MICCAI-Paper-Checklist 2 | > The checklist builds on the [machine learning reproducibility checklist](https://www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist.pdf), but is specific to MICCAI papers. 3 | > We also got lots of insights from 4 | > 5 | > - Mongan, John, Linda Moy, and Charles E. Kahn Jr. "Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A guide for authors and reviewers." Radiology: Artificial Intelligence (2020): e200029. 6 | > - Norgeot, Beau, Giorgio Quer, Brett K. Beaulieu-Jones, Ali Torkamani, Raquel Dias, Milena Gianfrancesco, Rima Arnaout et al. "Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist." Nature Medicine 26, no. 9 (2020): 1320-1324 7 | > - Reproducibility Criteria in [ELNMP 2020](https://2020.emnlp.org/call-for-papers) 8 | > 9 | 10 | ### Methods 11 | 12 | - A clear description of the mathematical setting, algorithm, and/or model. 13 | - Network architecture details 14 | - layer details in each block/module 15 | - the number of parameters 16 | - Explanation of evaluation metrics (with links to code) 17 | 18 | ### Experiments 19 | 20 | - Dataset 21 | - for public dataset, providing data sources, e.g, references and URL links 22 | - for private dataset, providing the data acquisition source and characteristics (e.g., device, contrast agent...), the eligibility description, the ground truth standard (e.g., qualifications and preparation of annotators), annotation tools, analysis of inter-rater variability 23 | - details of train / validation / test splits 24 | - Preprocessing steps 25 | - cropping strategy 26 | - resampling method for anisotropic data 27 | - intensity normalization method 28 | - registration method for multi-sequence/modality data 29 | - Training protocols 30 | - computing infrastructure (e.g., GPU name, number, memory) 31 | - patch size and patch sampling strategy 32 | - batch size 33 | - optimizer, learning rate and its decay schedule 34 | - loss function 35 | - data augmentation methods 36 | - stopping criteria, and optimal model selection criteria 37 | - training time 38 | - Testing steps 39 | - if using patch-based strategy, describing the patch aggregation method 40 | - inference time 41 | - Postprocessing steps 42 | - Ablation study 43 | 44 | ### Results 45 | - Quantitative analysis of cross validation results and/or testing set results 46 | - average and standard deviation of evaluation metrics 47 | - statistical analysis (e.g., statistical methods, significant levels...) 48 | 49 | - Qualitative analysis 50 | - box/violin plot, ROC curves 51 | - visualized examples of both successful and **failed** cases 52 | --------------------------------------------------------------------------------