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‎pages/platform/ai/notebook_tuto_12_image-segmentation-unet-tumors/guide.de-de.md

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title: AI Notebooks - Tutorial - Brain tumor segmentation using U-Net
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slug: notebooks/tuto-image-segmentation-unet-brain-tumors
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slug: notebooks/tuto-image-segmentation-unet-tumors
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excerpt: Implementing a Convolutional Neural Network for Brain Tumor Segmentation in Medical Imaging
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section: AI Notebooks - Tutorials
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order: 12
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routes:
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canonical: 'https://docs.ovh.com/gb/en/publiccloud/ai/notebooks/tuto-image-segmentation-unet-brain-tumors/'
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canonical: 'https://docs.ovh.com/gb/en/publiccloud/ai/notebooks/tuto-image-segmentation-unet-tumors/'
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updated: 2023-04-13
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#### Resources
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GPU is recommended because medical imaging is a training intensive task.
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Using GPUs is recommended because medical imaging is a training intensive task.
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> [!primary]
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### Launching a Jupyter notebook with "Miniconda" via CLI
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*If you do not have our CLI yet, follow [this guide](https://docs.ovh.com/de/publiccloud/ai/cli/install-client/) to install it.*
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*If you do not use our CLI yet, follow [this guide](https://docs.ovh.com/de/publiccloud/ai/cli/install-client/) to install it.*
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If you want to launch your notebook with the OVHcloud AI CLI, choose the `jupyterlab` editor and the `tensorflow` framework.
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‎pages/platform/ai/notebook_tuto_12_image-segmentation-unet-tumors/guide.en-asia.md

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title: AI Notebooks - Tutorial - Brain tumor segmentation using U-Net
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slug: notebooks/tuto-image-segmentation-unet-brain-tumors
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slug: notebooks/tuto-image-segmentation-unet-tumors
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excerpt: Implementing a Convolutional Neural Network for Brain Tumor Segmentation in Medical Imaging
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section: AI Notebooks - Tutorials
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order: 12
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#### Resources
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GPU is recommended because medical imaging is a training intensive task.
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Using GPUs is recommended because medical imaging is a training intensive task.
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> [!primary]
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### Launching a Jupyter notebook with "Miniconda" via CLI
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*If you do not have our CLI yet, follow [this guide](https://docs.ovh.com/asia/en/publiccloud/ai/cli/install-client/) to install it.*
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*If you do not use our CLI yet, follow [this guide](https://docs.ovh.com/asia/en/publiccloud/ai/cli/install-client/) to install it.*
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If you want to launch your notebook with the OVHcloud AI CLI, choose the `jupyterlab` editor and the `tensorflow` framework.
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‎pages/platform/ai/notebook_tuto_12_image-segmentation-unet-tumors/guide.en-au.md

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title: AI Notebooks - Tutorial - Brain tumor segmentation using U-Net
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slug: notebooks/tuto-image-segmentation-unet-brain-tumors
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slug: notebooks/tuto-image-segmentation-unet-tumors
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excerpt: Implementing a Convolutional Neural Network for Brain Tumor Segmentation in Medical Imaging
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section: AI Notebooks - Tutorials
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order: 12
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#### Resources
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GPU is recommended because medical imaging is a training intensive task.
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Using GPUs is recommended because medical imaging is a training intensive task.
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> [!primary]
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>
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### Launching a Jupyter notebook with "Miniconda" via CLI
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*If you do not have our CLI yet, follow [this guide](https://docs.ovh.com/au/en/publiccloud/ai/cli/install-client/) to install it.*
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*If you do not use our CLI yet, follow [this guide](https://docs.ovh.com/au/en/publiccloud/ai/cli/install-client/) to install it.*
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If you want to launch your notebook with the OVHcloud AI CLI, choose the `jupyterlab` editor and the `tensorflow` framework.
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‎pages/platform/ai/notebook_tuto_12_image-segmentation-unet-tumors/guide.en-ca.md

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title: AI Notebooks - Tutorial - Brain tumor segmentation using U-Net
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slug: notebooks/tuto-image-segmentation-unet-brain-tumors
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slug: notebooks/tuto-image-segmentation-unet-tumors
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excerpt: Implementing a Convolutional Neural Network for Brain Tumor Segmentation in Medical Imaging
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section: AI Notebooks - Tutorials
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order: 12
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#### Resources
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GPU is recommended because medical imaging is a training intensive task.
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Using GPUs is recommended because medical imaging is a training intensive task.
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> [!primary]
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### Launching a Jupyter notebook with "Miniconda" via CLI
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*If you do not have our CLI yet, follow [this guide](https://docs.ovh.com/ca/en/publiccloud/ai/cli/install-client/) to install it.*
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*If you do not use our CLI yet, follow [this guide](https://docs.ovh.com/ca/en/publiccloud/ai/cli/install-client/) to install it.*
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If you want to launch your notebook with the OVHcloud AI CLI, choose the `jupyterlab` editor and the `tensorflow` framework.
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‎pages/platform/ai/notebook_tuto_12_image-segmentation-unet-tumors/guide.en-gb.md

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title: AI Notebooks - Tutorial - Brain tumor segmentation using U-Net
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slug: notebooks/tuto-image-segmentation-unet-brain-tumors
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slug: notebooks/tuto-image-segmentation-unet-tumors
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excerpt: Implementing a Convolutional Neural Network for Brain Tumor Segmentation in Medical Imaging
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section: AI Notebooks - Tutorials
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order: 12
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#### Resources
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GPU is recommended because medical imaging is a training intensive task.
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Using GPUs is recommended because medical imaging is a training intensive task.
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> [!primary]
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### Launching a Jupyter notebook with "Miniconda" via CLI
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*If you do not have our CLI yet, follow [this guide](https://docs.ovh.com/gb/en/publiccloud/ai/cli/install-client/) to install it.*
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*If you do not use our CLI yet, follow [this guide](https://docs.ovh.com/gb/en/publiccloud/ai/cli/install-client/) to install it.*
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If you want to launch your notebook with the OVHcloud AI CLI, choose the `jupyterlab` editor and the `tensorflow` framework.
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## Go further
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There are many other tasks that exist in the computer vision field. Check our other tutorials to learn how to:
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There are many other tasks that exist in the computer vision field. Check our other tutorials to learn how to:
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- [Use Transfer Learning with ResNet50 for image classification](https://github.com/ovh/ai-training-examples/blob/main/notebooks/computer-vision/image-classification/tensorflow/resnet50/notebook-resnet-transfer-learning-image-classification.ipynb)
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‎pages/platform/ai/notebook_tuto_12_image-segmentation-unet-tumors/guide.en-ie.md

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title: AI Notebooks - Tutorial - Brain tumor segmentation using U-Net
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slug: notebooks/tuto-image-segmentation-unet-brain-tumors
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slug: notebooks/tuto-image-segmentation-unet-tumors
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excerpt: Implementing a Convolutional Neural Network for Brain Tumor Segmentation in Medical Imaging
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section: AI Notebooks - Tutorials
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order: 12
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#### Resources
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GPU is recommended because medical imaging is a training intensive task.
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Using GPUs is recommended because medical imaging is a training intensive task.
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> [!primary]
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### Launching a Jupyter notebook with "Miniconda" via CLI
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*If you do not have our CLI yet, follow [this guide](https://docs.ovh.com/ie/en/publiccloud/ai/cli/install-client/) to install it.*
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*If you do not use our CLI yet, follow [this guide](https://docs.ovh.com/ie/en/publiccloud/ai/cli/install-client/) to install it.*
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If you want to launch your notebook with the OVHcloud AI CLI, choose the `jupyterlab` editor and the `tensorflow` framework.
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‎pages/platform/ai/notebook_tuto_12_image-segmentation-unet-tumors/guide.en-sg.md

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title: AI Notebooks - Tutorial - Brain tumor segmentation using U-Net
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slug: notebooks/tuto-image-segmentation-unet-brain-tumors
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slug: notebooks/tuto-image-segmentation-unet-tumors
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excerpt: Implementing a Convolutional Neural Network for Brain Tumor Segmentation in Medical Imaging
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section: AI Notebooks - Tutorials
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order: 12
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#### Resources
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GPU is recommended because medical imaging is a training intensive task.
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Using GPUs is recommended because medical imaging is a training intensive task.
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> [!primary]
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### Launching a Jupyter notebook with "Miniconda" via CLI
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*If you do not have our CLI yet, follow [this guide](https://docs.ovh.com/sg/en/publiccloud/ai/cli/install-client/) to install it.*
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*If you do not use our CLI yet, follow [this guide](https://docs.ovh.com/sg/en/publiccloud/ai/cli/install-client/) to install it.*
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If you want to launch your notebook with the OVHcloud AI CLI, choose the `jupyterlab` editor and the `tensorflow` framework.
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‎pages/platform/ai/notebook_tuto_12_image-segmentation-unet-tumors/guide.en-us.md

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title: AI Notebooks - Tutorial - Brain tumor segmentation using U-Net
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slug: notebooks/tuto-image-segmentation-unet-brain-tumors
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slug: notebooks/tuto-image-segmentation-unet-tumors
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excerpt: Implementing a Convolutional Neural Network for Brain Tumor Segmentation in Medical Imaging
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section: AI Notebooks - Tutorials
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order: 12
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#### Resources
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GPU is recommended because medical imaging is a training intensive task.
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Using GPUs is recommended because medical imaging is a training intensive task.
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> [!primary]
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### Launching a Jupyter notebook with "Miniconda" via CLI
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*If you do not have our CLI yet, follow [this guide](https://docs.ovh.com/us/en/publiccloud/ai/cli/install-client/) to install it.*
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*If you do not use our CLI yet, follow [this guide](https://docs.ovh.com/us/en/publiccloud/ai/cli/install-client/) to install it.*
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If you want to launch your notebook with the OVHcloud AI CLI, choose the `jupyterlab` editor and the `tensorflow` framework.
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‎pages/platform/ai/notebook_tuto_12_image-segmentation-unet-tumors/guide.es-es.md

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title: AI Notebooks - Tutorial - Brain tumor segmentation using U-Net
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slug: notebooks/tuto-image-segmentation-unet-brain-tumors
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slug: notebooks/tuto-image-segmentation-unet-tumors
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excerpt: Implementing a Convolutional Neural Network for Brain Tumor Segmentation in Medical Imaging
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section: AI Notebooks - Tutorials
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order: 12
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routes:
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canonical: 'https://docs.ovh.com/gb/en/publiccloud/ai/notebooks/tuto-image-segmentation-unet-brain-tumors/'
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canonical: 'https://docs.ovh.com/gb/en/publiccloud/ai/notebooks/tuto-image-segmentation-unet-tumors/'
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updated: 2023-04-13
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---
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@@ -53,7 +53,7 @@ In this tutorial, the `tensorflow` framework is used. If you use another environ
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#### Resources
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GPU is recommended because medical imaging is a training intensive task.
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Using GPUs is recommended because medical imaging is a training intensive task.
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> [!primary]
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### Launching a Jupyter notebook with "Miniconda" via CLI
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*If you do not have our CLI yet, follow [this guide](https://docs.ovh.com/es/publiccloud/ai/cli/install-client/) to install it.*
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*If you do not use our CLI yet, follow [this guide](https://docs.ovh.com/es/publiccloud/ai/cli/install-client/) to install it.*
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If you want to launch your notebook with the OVHcloud AI CLI, choose the `jupyterlab` editor and the `tensorflow` framework.
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‎pages/platform/ai/notebook_tuto_12_image-segmentation-unet-tumors/guide.es-us.md

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title: AI Notebooks - Tutorial - Brain tumor segmentation using U-Net
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slug: notebooks/tuto-image-segmentation-unet-brain-tumors
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slug: notebooks/tuto-image-segmentation-unet-tumors
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excerpt: Implementing a Convolutional Neural Network for Brain Tumor Segmentation in Medical Imaging
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section: AI Notebooks - Tutorials
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order: 12
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routes:
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canonical: 'https://docs.ovh.com/gb/en/publiccloud/ai/notebooks/tuto-image-segmentation-unet-brain-tumors/'
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canonical: 'https://docs.ovh.com/gb/en/publiccloud/ai/notebooks/tuto-image-segmentation-unet-tumors/'
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updated: 2023-04-13
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#### Resources
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GPU is recommended because medical imaging is a training intensive task.
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Using GPUs is recommended because medical imaging is a training intensive task.
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> [!primary]
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### Launching a Jupyter notebook with "Miniconda" via CLI
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*If you do not have our CLI yet, follow [this guide](https://docs.ovh.com/us/es/publiccloud/ai/cli/install-client/) to install it.*
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*If you do not use our CLI yet, follow [this guide](https://docs.ovh.com/us/es/publiccloud/ai/cli/install-client/) to install it.*
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If you want to launch your notebook with the OVHcloud AI CLI, choose the `jupyterlab` editor and the `tensorflow` framework.
6868

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