Category: Teaching

  • Signal Filtering

    Introducing lectures: Generalities, Analog filtering, Numerical Filtering (in french)

  • How many e-mails?

    As my inbox is nearing its storage limit, I’m getting ready to garbage can some of my mail. Traditionally, I’d garbage can the biggest e-mails. But today, I’ve decided to trash my old sent e-mails. As a result, I realize that from May 5, 2020 to May 5, 2023, I wrote more than 10,000 e-mails!…

  • CLANU 2021

    Ressources pour le projet CLANU partie informatique pour la classification d’images d’IRM provennant de 3 séquences et coupées suivant 3 plans (problème de 9 classes) Enoncé de la partie informatique et code source C++

  • Protected: DLMI23 Gala Photo

    There is no excerpt because this is a protected post.

  • DLMI 23

    Information and Resources Lectures Monday : video video video video Tuesday : video video Wednesday: video video Thursday: video video Friday: video video Hands-on Challenge: Photos, just for souvenir restricted access (particpants only) Gala photos gallery post Lectures photos gallery post Group photos gallery post Round Table photo gallery post

  • 4th Edition of the Deep Learning Medical Imaging School

    DLMI23: Fourth edition of the Deep learning for medical imaging school – Lyon April 17-21 2023 DeepImaging 2023 is organized by the LabEx Primes, Creatis, LabHC laboratories, the University of Sherbrooke and the ETS of Montreal. This school is intended for medical imaging beginners and experts (students, post-docs, research professionals, and professors) who are…

  • 3rd edition of Deep Learning Medical Image School 2022

    The third edition of the school is almost done. Thanks to participants and the Montreal organizing committee! We had an exciting week. The next edition (4th) will take place in Lyon. Dates are coming soon. Link to 3rd edition ressources:

  • Machine learning medical image classification notebooks

    Machine learning medical image classification notebooks

    Image classification is an interesting challenge that requires many images for training. Here we propose to explore 4 different machine learning classification approaches (from random forest to ResNet) on an image set created from the IXI data set using famous python packages: scikit-learn and Keras/TensorFlow. This example is split into 3 parts. The first notebook…

  • Practice on medical image segmentation with UNet

    Here are the hands-on materials to practice deep learning segmentation with UNet (Keras/TF2): This archive contains jupyter notebooks and necessary functions (i.e. all the code in python). See another post to install TF2/Keras with conda. The data are available here: dlss21_ho4_data.tar.gz The command line to extract the data is: $ tar xzf dlss21_ho4_data.tar.gz The…

  • 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…