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McMedHacks: Week 1/Introduction to classical machine learning

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Activités de formation

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. 

Theme and program of 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

Activities

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Featured project

Effect of oxygen pressure in cancerous tissue cells on radiotherapy treatments

Student member : Corinne Chouinard

Radiotherapy treatments currently used in the clinical field are rarely modified. They generally consist of a global therapy of 50 grays, fractionated in five treatments of two grays every week for five weeks.
Thus, it could be worthwhile to develop a numeric tool, based on mathematical models found in the literature, in order to compare different types of treatment without having to test them on real tissues. Several parameters are known to alter the tissue response after irradiation including oxygen

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