aide
Deep learning for medical imaging
Cours donné en anglais
Olivier Colliot (CNRS), Maria Vakalopoulou (CentraleSupelec)
Objective of the course:
Medical imaging technologies provide unparalleled means to study structure and function of the human body in vivo. Interpretation of medical images is difficult due to the need to take into account three-dimensional, time-varying information from multiple types of medical images. Artificial intelligence (AI) holds great promises for assisting in the interpretation and medical imaging is one of the areas where AI is expected to lead to the most important successes. In the past years, deep learning technologies have led to impressive advances in medical image processing and interpretation.
This course covers both theoretical and practical aspects of deep learning for medical imaging. It covers the main tasks involved in medical image analysis (classification, segmentation, registration, generative models...) for which state-of-the-art deep learning techniques are presented, alongside some more traditional image processing and machine learning approaches. Examples of different types of medical imaging applications (brain, cardiac...) will also be provided.
This course covers both theoretical and practical aspects of deep learning for medical imaging. It covers the main tasks involved in medical image analysis (classification, segmentation, registration, generative models...) for which state-of-the-art deep learning techniques are presented, alongside some more traditional image processing and machine learning approaches. Examples of different types of medical imaging applications (brain, cardiac...) will also be provided.
References :
Website: http://www.aramislab.fr/teaching/DLMI-2020-2021/
Books: I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 2016.
Datasets: There are plenty of public datasets for medical imaging. Some curated lists can be found at : https://github.com/beamandrew/medical-data https://sites.google.com/site/aacruzr/image-datasets https://github.com/sfikas/medical-imaging-datasets, https://grand-challenge.org/challenges/
Software tools: pyTorch, TensorFlow
Related conferences: MICCAI (Medical Image Computing and Computer-Assisted Intervention), IPMI (Information Processing in Medical Imaging), ISBI (International Symposium on Biomedical Imaging), MIDL (Medical Imaging with Deep Learning)
Books: I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 2016.
Datasets: There are plenty of public datasets for medical imaging. Some curated lists can be found at : https://github.com/beamandrew/medical-data https://sites.google.com/site/aacruzr/image-datasets https://github.com/sfikas/medical-imaging-datasets, https://grand-challenge.org/challenges/
Software tools: pyTorch, TensorFlow
Related conferences: MICCAI (Medical Image Computing and Computer-Assisted Intervention), IPMI (Information Processing in Medical Imaging), ISBI (International Symposium on Biomedical Imaging), MIDL (Medical Imaging with Deep Learning)
We expect that by the end of the course, the students will:
- have knowledge of state-of-the-art deep learning techniques for medical imaging
- have a deeper understanding of deep learning methods, applicable not only to medical images but also other types of data
- have knowledge of state-of-the-art deep learning techniques for medical imaging
- have a deeper understanding of deep learning methods, applicable not only to medical images but also other types of data
- know how to build and validate deep learning models for medical images
Topics:
- Introduction to medical imaging
- Classification
- Detection
- Segmentation
- Registration
- Denoising and reconstruction
- Generative models (autoencoders, GANs)
- Validation, interpretation and reproducibility
Prerequisites :
There is no official prerequisite for this course. However, the students are expected to have a background in statistics and computer programming.
Organization of courses:
- Place: CentraleSupelec
- 9x 3h (either 3h lecture or 1h30 lecture/1h30 lab)
- Usage of a personal laptop is encouraged for practical sessions
Validation:
Article presentation and project