Thomas Grenier

Category: Teaching

  • Using Jupyter Lab and Tensorflow within a conda environment

    1- before launching jupyter, check the environment kernel $ jupyter kernelspec list 2- if nothing appears looking “TF2.6”, add the kernel to jupyter by: $ ipython kernel install –name “TF2.6” –user 3- now you should see TF2.6 with the command: $ jupyter kernelspec list 4 -you can repeat the above kernel installation for other kernels…

  • Nouveau au département GE ?

    Une petite présentation de l’informatique et des systèmes au département Génie Électrique de l’INSA Lyon. (pdf) Une petite visite des salles à 360° ? ici!

  • Simple Filtering and Segmentation of medical image

    Here is the notebook to start with SimpleITK and basics such as N4 bias field correction on MRI and segmentation using region growing and python programming. We also study the k-means segmentation. Python notebook : (zip)

  • Medical Deep Imaging spring school 2021

    Our third edition of the spring/summer school on deep learning for medical images will be ‘virtual’ and from 19 to 24 of April 2021. Visit the official website here. As the previous edition, there are some lectures, practices, and social events. Most of the content will be available after the school. The first edition web…

  • Install Tensorflow2 or PyTorch/MONAI-dev with conda

    Create a TensorFlow conda environment A short summary of conda utilization: This procedure is the same for PyTorch, just download monai-dev.yml and adapt the previous lines. This file includes MONAI-dev prerequisits. then finalize the MONAI-dev installation by first activate your env and then installing the MOANI-dev using (more information here):

  • A very fast introduction to image processing

    Here are some slides to start with images and fundamental processing (pdf) There are also some funny basic practices using python. You may need to set up a working conda environment. For editing your py files, spyder or PyCharm are nice and efficient python IDEs. Activate a conda environment To create an environment with all…

  • Introduction to UNet for image segmentation (TF1)

    The short introduction to UNet and its architecture (pdf) The proposed code work fine with Tensorflow 1.15 and keras (almostly outdated…). Download this full archive with code, data, and pre-trained model (214 Mo, TP_UNET_FULL.zip). Then, use the notebooks in notebooks_local directory.

  • Medical Deep Imaging spring school 2019

    Do you want to found interesting materials to begin with deep learning for medical applications? Have a look here Materials are in Hands-on session page. Even if you don’t have a GPU card or an efficient installation of frameworks as TensorFlow or PyTorch, you can run the tutorials with your CPU. For beginners (and ones who…

  • Introduction to C and IRQ micro controller programming

    A short introduction to C programming of microcontrollers, including basics for IRQ and ISR design with gnu C. Bare-metal programming. C_uCuP Some sources to understand keywords as static, volatile, extern,… are available in the zip file: sources

  • Introduction to OpenMP

    OpenMP is widely use for concurrency programming with C/C++/fortran. Here are some exercices to understand how to use OpenMP. They are inspired from OpenMP tutorial but in C++. OpenMP lab Download the source files: OpenMP Source files