Treffer: Faking_it team! BraTS submissions. ; How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation ; Generalisation of Segmentation Using Generative Adversarial Networks ; Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentation ; Brain Tumour Removing and Missing Modality Generation using 3D Wavelet Diffusion Model

👋 Faking_it team! BraTS submissions 🎬  ## 💡 Key Solutions (each subpage contains all the steps to reproduce the solutions): - **🥇 BraTS 2023 Task 1:** [Adult Glioma Segmentation](BraTS2023_Task1.md)- **🥇BraTS-ISBI 2024 GoAT:** [Generalizability Across Tumors Challenge](BraTS2024-ISBI_GoAT.md)- **🥇BraTS 2024 Task 1:** [Adult Glioma Post Treatment](BraTS2024_Task1.md)- **🥉BraTS 2024 Task 3:** [Meningioma Radiotherapy](BraTS2024_Task3.md)- **🏅BraTS 2024 Task 7:** [Synthesis (Global) - Missing MRI ](./BraTS2024_Task7.md)-> [Check out poster! ](./imgs/MICCAI2024-Poster-Task7_8.pdf)- **🥈BraTS 2024 Task 8:** [Synthesis (Local) - Inpainting](./BraTS2024_Task8.md) -> [Check out poster! ](./imgs/MICCAI2024-Poster-Task7_8.pdf) ✅ This repository contains the code and all the steps to reproduce the results of the submissions to BraTS 2023 Task 1, BraTS-ISBI 2024 GoAT, BraTS 2024 Tasks 1, 3, 7 and 8. ✅ Note that BraTS 2023 Task 1, BraTS-ISBI 2024 GoAT BraTS 2024 Tasks 1 and 3 are segmentation tasks and BraTS 2024 Tasks 7 and 8 are synthetic generation (using WDM 3D). ### :star_struck: We have released the trained weights! :partying_face: 💾 You can download them at . You just need to place them in the correct place 🤓 ## Before running any experiments: 💻 For better experience, you should create a conda environment and have a machine with GPU. ### Segmentation tasks: ⚠️16GB of VRAM might be enough, however, we recomend using a GPU with 24GB. Be carefull with the amount of RAM you can use, as our code load the entire dataset to memory by default for faster training, but it might not be suitable for your machine. To reduce this, look into the data loaders. **💻 To create the conda environment:** 1. conda create -n BraTS_solutions python=3.11.92. pip install: 1. pip3 install torch torchvision torchaudio 2. pip install monai 3. pip install nilearn 4. pip install nibabel 5. pip install matplotlib 6. pip install pathlib 7. pip install einops 8. pip install tqdm 9. pip install SimpleITK 10. pip ...