Learn medical image analysis and deep learning in Python
June 11th – August 3rd, 2022
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McMedHacks 2022
McMedHacks is back!
McMedHacks is an 8-week-long program that aims to teach students and researchers fundamentals of medical image analysis and deep learning in Python. It consists of a series of in-depth workshop demos with Google Colab and seminar series given by leaders in the field. McMedHacks is a program organized by the EngerLab at McGill Medical Physics Unit and the Lady Davis Institute. EngerLab is a part of McGill Centre for Translational Research in Cancer as well as the Cancer Research Network. Last year’s participants can be found here!
NEW to this year’s edition:
- Three (prep) weeks prior to June 11th on Intro to Python Programming for Python beginners (See McMedHacks 2022 Program below).
- In-depth introduction in popular deep learning framework PyTorch.
- Step-by-step introduction from classical machine learning to traditional deep learning to advanced techniques and their applications in medical image analysis.
- EXTRA sessions during weekdays on top of regular sessions on the weekend on special techniques.
Learning Objectives:
- Familiarity with Fundamentals of image analysis
- Familiarity with Fundamentals of machine learning
- Introducing a deep learning framework (Pytorch)
- Introducing Fully supervised medical image segmentation
- Introducing Concepts in weakly and self-supervised learning
- Radiomics and Treatment Outcome Prediction
McMedHacks 2022 Workshop Program
Prep Weeks: Intro to Python Programming (Optional)
May 16th (Mon): Introduction to Python
May 23rd (Mon): Object-oriented programming with Python
June 6th (Mon): Scientific programming with Python
Week 1: Introduction to classical machine learning
June 12th (Sun): Introduction to classical machine learning
June 15th (Wed): Classical machine learning: A case study XGBoost
June 17th (Fri): Introduction to image processing: Image IO and image transformations
Week 2: Fundamentals of Image (Pre-)Processing
June 19th (Sun): Fundamentals of Image (Pre-)Processing, Quality assurance prior to deep learning model training
Week 3: Deep learning fundamentals
June 26th (Sun): Deep learning fundamentals
June 29th (Wed): Deep learning framework (PyTorch): Tensors and Autograd
Week 4: Deep learning framework (PyTorch): Model training and evaluation
July 3rd (Sun): Deep learning framework (PyTorch): Model training and evaluation
July 6th (Wed): Deep learning framework (PyTorch): Implement your own image classifier
Week 5: Fully Supervised Medical Image Segmentation
July 17th (Sun): Fully Supervised Medical Image Segmentation
July 20th (Wed): Deep learning framework (PyTorch): Tensorboard
Week 6: Deep learning framework (PyTorch): Implement U-Net
July 24th (Sun): Deep learning framework (PyTorch): Implement U-Net
July 27th (Wed): Deep learning model generalizability: pitfalls and best practices
Week 7: Supervised, weakly supervised, semi-supervised, and unsupervised learning
July 31st (Sun): Supervised, weakly supervised, semi-supervised, and unsupervised learning
August 3rd (Wed): Radiomics and Treatment Outcome Prediction
Week 8: Closing ceremony
Our 2021 McMedHacks Workshop Participants
In its first edition in 2021, McMedHacks gained 356 registrations from participants of 38 different countries from undergraduates, to PhDs and MDs. A vast number of disciplines and professions were represented, dominated by medical physics students, academic, and clinical medical physicists. The program received high participant feedback average scores of 4.768, 4.478, 4.579, 4.292, 4.84 out of five for the qualities of presentation, workshop session, tutorial and mentor, assignments, and course delivery, respectively.
with various levels of education
from 38+ countries
Our Sponsors
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This workshop series and hackathon would not be possible without the generous support of our sponsors. Thank you!!