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+# rocaseg - Robust Cartilage Segmentation from MRI
+
+Source code for Panfilov et al. "Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation", https://arxiv.org/abs/1908.04126v3.
+
+<p align="center">
+<img src="github_image.png" width="700" alt="Overview"/> 
+</p>
+
+### Important!
+
+The camera-ready version contained a bug in Dice score computation for tibial cartilage on Dataset C. Please, refer to the arXiv version for the corrected values - https://arxiv.org/abs/1908.04126v3.
+
+### Description
+
+1. To reproduce the experiments from the article one needs to have access to
+ OAI iMorphics, OKOA, and MAKNEE datasets.
+
+2. Download code from this repository.
+ 
+3. Create a fresh Conda environment using `environment.yml`. Install the downloaded
+ code as a Python module.
+
+4. `datasets/prepare_dataset_...` files show how the raw data is converted into the
+ format supported by the training and the inference pipelines.
+ 
+5. The structure of the project has to be as follows:
+    ```
+    ./project/
+        | ./data_raw/  # raw scans and annotations
+             | ./OAI_iMorphics_scans/
+             | ./OAI_iMorphics_annotations/
+             | ./OKOA/
+             | ./MAKNEE/
+        | ./data/  # preprocessed scans and annotations
+        | ./src/ (this repository)
+        | ./results/  # models' weights, intermediate and final results 
+             | ./0_baseline/
+                  | ./weights/
+                  | ...
+             | ./1_mixup/
+             | ./2_mixup_nowd/
+             | ./3_uda1/
+             | ./4_uda2/
+             | ./5_uda1_mixup_nowd/
+    ```
+
+6. File `scripts/runner.sh` contains the complete description of the workflow.
+
+7. Statistical testing is implemented in `notebooks/Statistical_tests.ipynb`.
+
+8. Pretrained models are available at https://drive.google.com/open?id=1f-gZ2wCf55OVjgA8oXd7xttGVW5DUUcU .
+
+### Legal aspects
+
+This code is freely available only for research purposes.
+
+The software has not been certified as a medical device and, therefore, must not be used
+for diagnostic purposes. 
+
+Commercial use of the provided code and the pre-trained models is strictly prohibited,
+since they were developed using the medical datasets under restrictive licenses.   
+
+### Cite this work
+
+```
+@InProceedings{Panfilov_2019_ICCV_Workshops,
+  author = {Panfilov, Egor and Tiulpin, Aleksei and Klein, Stefan and Nieminen, Miika T. and Saarakkala, Simo},
+  title = {Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation},
+  booktitle = {The IEEE International Conference on Computer Vision (ICCV) Workshops},
+  month = {Oct},
+  year = {2019}
+}
+```