The McMedHacks program consists of 8 weeks of presentations and interactive workshops that will help you learn the fundamentals of medical image analysis and deep learning in Python. Each week will have one presentation session and one workshop.
During the presentations sessions leaders in industry and researchers with cutting edge medical image analysis and deep learning projects will present their work. The purpose of these presentation sessions is to motivate and inspire you for the upcoming workshop.
The interactive workshops will be tutorials and demonstrations of how to apply deep learning based analysis to various medical imaging modalities. Participants can follow along and modify the code during the workshops using Google Colab.
The workshops will take place from June 12th until July 31st. After the workshops we will be hosting the McMedHacks hackathon as an opportunity to practice your skills in a friendly competition. More information about the hackathon is coming soon.
Breakdown:
Week 1 – Intro to Python for deep learning
Presentation – June 12th 14:00-15:30 EDT
Opening ceremony: The Use of Artificial Intelligence in Radiotherapy
Speaker: Shirin Abbasinejad Enger, PhD
Workshop – June 13th 10:00-11:30 EDT
Introduction to Python for deep learning
Instructors: Yujing Zou
Introduction to Python for deep learning
Instructors: Luca Weishaupt,
Introduction to Pytorch
Instructor: Hossein Jafarzadeh
Week 2 – Intro to deep learning
Presentation – June 19th 10:00 -11:30 EDT
Visual Analytics for Cancer Radiotherapy
Speaker: Renata Raidou, PhD
The past, present, and future of machine learning in medical imaging and related applications
Speaker: Roy Keyes, PhD
Workshop – June 20th 10:00-11:30 EDT
Introduction to deep learning
Instructor: Ximeng Mao
Week 3 – Histopathology and image analysis
Presentation – June 25th 12:00-13:30 EDT
Experiences in medical software entrepreneurship
Speaker: Karl Otto, PhD
Building and deploying AI models for clinical practice
Speaker: Joshua Giambatista, MD;
Medical imaging machine learning pipelines for production
Speaker: Carter Kolbeck M.Sc.;
Shipping AI software in a regulated medical device industry
Speaker: Jonathan Giambatista, B.A. Sc.
Workshop – June 27th 10:00-11:30 EDT
Digital Histopathological Image analysis
Instructor: Luca Weishaupt
Brief Intro to Histopathological Images
Instructor: Yujing Zou
Evaluation of machine learning models
Instructor: Farhad Maleki, PhD
Week 4 – CT and semantic segmentation
Presentation – July 3rd 10:00-11:30 EDT
Multi-modal Machine Learning for CT – From an example to Generality
Speaker: Christina Gillmann, PhD
Medical Imaging Analysis and Deep Learning Applications in Brachytherapy
Speaker: Christopher Deufel, PhD
Workshop – July 4th 10:00-11:30 EDT
3D image augmentation and its use case in semantic segmentation
Instructor: Farhad Maleki, PhD
Week 5 – Ultrasound and instance segmentation
Presentation – July 10th 10:00-11:30 EDT
Artificial Intelligence in Medical Imaging: Image-based biomarkers & beyond
Speaker: Reza Forghani, PhD
Workshop – July 11th 10:00-11:30 EDT
Ultrasound and instance segmentation
Instructors: Peter Watson, PhD, Yujing Zou, Luca Weishaupt
Week 6 – MR and GAN-based segmentation
Presentation – July 16th 10:00-11:30 EDT
Academia to industry and the role AI has played in both
Speaker: Andre Diamant, PhD
MEDomics: integrative data modelling in oncology
Speaker: Martin Vallières, PhD
A Machine Learning Radiation Therapy System from Bench to Bedside
Speaker: Professor Chris McIntosh
Workshop – July 18th 10:00-11:30 EDT
MR and GAN-based segmentation
Instructors: Yiping Wang
Week 7 – Multimodality and outcome prediction
Presentation – July 24th 10:00-11:30 EDT
Medical Imaging in the age of AI
Speaker: Lei Xing, PhD
Data mining in radiotherapy: understanding the link between dose and long-term side effects
Speaker: Marianne Aznar, PhD
Delivering value in healthcare with AI
Speaker: Mark Gooding, PhD
Workshop – July 28th 19:00-20:30 EDT
Multimodality and outcome prediction
Instructor: Ibrahim Chamseddine, PhD
Week 8 – Closing ceremony
Presentation – July 31st 10:00-12:15 EDT
Conditional Generation of Medical Images via Disentangled Adversarial Inference
Speaker: Mohammad Havaei, PhD
A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images
Speaker: Issam Laradji, PhD
Distributed Deep Learning Techniques for Medical Imaging
Speaker: Ken Chang, PhD
Future directions in machine learning and their application in healthcare
Speaker: Issam El Naqa, PhD