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McMedHacks 2023 Program
(10:30 AM – 12:30 PM Eastern Daylight Time of the following dates)
Week 0 (June 5-9): Prep Week
- Title: Introduction to Python
- Type: Self-Learning Module
- Title: Object-Oriented Programming with Python
- Type: Self-Learning Module
- Title: Scientific Programming with Python
- Type: Self-Learning Module
Week 1 (June 10-16): Introduction to Classical Machine Learning
Seminar (Saturday, June 10)
- Title: Opening Ceremony
- Type: Live Session
- Speakers: Dr. Farhad Maleki & Dr. Stine Korreman
Workshops (Sunday, June 11)
- Title: Introduction to Classical Machine Learning
- Type: Live Session
- Speaker: Dr. Zahra Azizi, MD, MSc
- Title: Classical Machine Learning: A Case Study Gradient Boosting Machine
- Type: Self Learning Module
- Title: Introduction to Image Processing: Image IO and Image Transformations
- Type: Self Learning Module
Week 2 (June 17-23): Deep Learning Fundamentals for Image Processing
Seminar (Saturday, June 17)
- Title: Novel Technologies for the Advancement of Precision Oncology
- Type: Live Session
- Speakers: Dr. Shirin Abbasinejad Enger & Dr. Kostas Trakas
Workshop (Sunday, June 18)
- Title: Deep Learning Fundamentals
- Type: Live Session
- Speaker: Dr. Parsa Bagherzadeh
- Title: Fundamentals of Image (Pre-)Processing: Quality Assurance Prior to Deep Learning Model Training
- Type: Self Learning Module
Week 3 (June 24-30): Deep Learning Segmentation
Seminar (Saturday, June 24th)
- Title: Co-Design and Data Sharing with a Patient Portal to Facilitate AI Research in Healthcare
- Type: Live Session
- Speakers: Dr. John Kildea
Workshop (Sunday, June 25th)
- Title: Fully Supervised Medical Image Segmentation
- Type: Live Session
- Speakers: Dr. Pouria Rouzrokh
- Title: Deep Learning Framework (PyTorch): Tensors and Autograd
- Type: Self Learning Module
Week 4 (July 1-7): Transfer Learning for Medical Imaging
Seminar (Saturday, July 1st)
- Title: Generative Models: The Case of Variational Auto-Encoders
- Type: Live Session
- Speakers: Dr. Mohammad Havaei
Workshops (Sunday, July 2nd)
- Title: Transfer Learning in Medical Imaging
- Type: Live Session
- Speaker: Dr. Bardia Khosravi
- Title: Deep learning Framework (PyTorch): Implement Your Own Image Classifier
- Type: Self Learning Module
- Title: Deep Learning Framework (PyTorch): Transfer Learning for Designing Your Own Image Classifier
- Type: Self Learning Module
Week 5 (July 8-14): Self-Supervision
Seminar (Saturday, July 8th)
- Title: NLP Buy vs Build in a Post-ChatGPT World
- Type: Live Session
- Speakers: Dr. Roy Keyes
Workshops (Sunday, July 9th)
- Title: Self-Supervision
- Type: Live Session
- Speakers: Dr. Najeeb Khan
- Title: Deep Learning Framework (PyTorch): Tensorboard & Pytorch Lightning (extra resources)
- Type: Self Learning Module
Week 6 (July 15-21): Introduction to Natural Language Processing (NLP)
Seminar (Saturday, July 15th)
- Title: Deep Learning-Based Auto-Segmentation of Organs at Risk in Cranial Spinal Irradiation of Pediatric Patients
- Type: Live Session
- Speakers: Dr. James Tsui
Workshop (Sunday, July 16th)
- Title: Natural Language Processing
- Type: Live Session
- Speakers: Dr. Parsa Bagherzadeh
Week 7 (July 22-28): Transformers
Seminar (Saturday, July 22nd)
- Title: An Approach for Sustainable AI Ecosystem for Medical Imaging in Resource Limited Settings & Medical Decision Transformers
- Type: Live Session
- Speakers: Dr. Udunna Anazodo & Dr. Samira Ebrahimi Kahou
Workshops (Sunday, July 23rd)
- Title: Transformers
- Type: Live Session
- Speakers: Dr. Parsa Bagherzadeh
Week 8 (July 29-August 4): Model Generalizability
Seminar (Saturday July 29th)
- Title: Translating Radiomics from a Research Topic Into Actionable Insights for Solid Tumor Care
- Type: Live Session
- Speakers: Dr. Wim Vos & Dr. Leonard Wee
Workshop (Sunday July 30th)
- Title: Model Generalizability
- Type: Live Session
- Speakers: Dr. Laya Rafiee
Week 8 (August 5): Closing Ceremony
Seminar (Saturday, August 5th)
- Title: Deep Learning in Cancer Radiation Therapy & Closing Remarks
- Type: Live Session
- Speakers: Dr. Xun Jia & Dr. Du Hao
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