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Learn medical image analysis and deep learning in Python

June 11th – August 6th, 2022

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Week 3 Workshop: Deep learning framework (Pytorch): Tensors and Autograd

McMedHacks is back!

McMedHacks 2022 Statistics

McMedHacks 2022 attracted 1000+ registrations from 62 countries!

With levels of educations:

From fields of studies and domains

They heard about McMedHacks through

McMedHacks 2022 Program
(10:30 AM – 12:30 PM Eastern Daylight Time of the following dates)

Prep Weeks: Intro to Python Programming (Optional)

May 16th (Mon): Introduction to Python

Instructor: Dr. Farhad Maleki

May 23rd (Mon): Object-oriented programming with Python

Instructor: Hunter Buckhorn

June 6th (Mon): Scientific programming with Python

Instructor: Saman Rahbar

Week 1: Introduction to Classical Machine Learning
June 11th (Sat): Opening Ceremony

Speakers: Dr. Shirin Abbasinejad Enger and Dr. Gerald Batist

Workshops
June 12th (Sun): Introduction to Classical Machine Learning

Instructor: Dr. Najeeb Khan

June 15th (Wed): Classical Machine Learning: A Case Study XGBoost

Instructor: Dr. Zahra Azizi

June 17th (Fri): Introduction to Image Processing: Image IO and Image Transformations

Instructors: Sébastien Quetin and Yujing Zou

Week 2: Fundamentals of Image (Pre-)Processing

Seminars

June 18th (Sat): Deep Learning Model Generalizability & Clinical Translation from an Academic Researcher’s Perspective

Speaker: Dr. Julien Cohen-Adad

How does pre-processing of medical images affect the model generalizability, especially for training multi-modal medical imaging tasks?

June 18th (Sat): Image-guided brachytherapy

Speaker: Dr. Marjory Jolicoeur

Workshops

June 19th (Sun): Fundamentals of Image (Pre-)Processing: Quality Assurance Prior to Deep Learning Model Training 

Instructor: Kiri Stern

Week 3: Deep Learning Fundamentals

Seminars

June 25th (Sat): Using prediction models in clinical practice:
Introduction of an NTCP-model based selection approach for proton therapy in esophageal cancer

Speaker: Dr. Maaike Berbee

To date prediction models are slowly finding their way into clinical practice. They are for example being used to facilitate shared decision making and, in the Netherlands, to selectively offer proton therapy to those patients who are expected to benefit from proton therapy. In this talk I will show how we validated a model to predict 2-year mortality in order to implement a model-based selection approach for proton therapy in esophageal cancer patients. Besides going through this example we will discuss the questions “Why do we need models of treatment effects in medicine” and “How to get models into wide use in the clinic”.

June 25th (Sat): Deep learning for radiotherapy outcome prediction using dose data

Speaker: Dr. Ane Appelt

Modern radiotherapy uses complex dose planning software to estimate and optimise the radiation dose delivered to the individual patient, based on 3D anatomical imaging. Consequently, we have detailed 3D data on the distribution of radiation dose to different parts of the body for every patient treated for radiotherapy – and, if we can predict who will get tumour control and/or toxicity, we can adjust this dose distribution to achieve the optimal ratio between the two. However, most traditional methods for outcome prediction only use very reduced data representations as input to the prediction models (e.g. ‘mean dose to an organ’), not the full 3D data. I’ll describe how we might apply CNN-based methods for classification to the problem of identifying ‘toxic’ radiation dose distributions; treating the dose information as just another type of image. But I’ll also discuss some of the pitfalls of this approach, and the large – and still unsolved – step of feeding the model output back into the radiotherapy treatment planning process.

Workshops

June 26th (Sun): Deep Learning Fundamentals

Instructor: Hunter Buckhorn

June 29th (Wed): Deep Learning Framework (PyTorch): Tensors and Autograd

Instructors: Sébastien Quetin and Yujing Zou

Week 4: Deep Learning Framework (PyTorch): Model Training and Evaluation

Seminars

July 2nd (Sat): Fairness & Bias in Data mining in radiotherapy

Speaker: Dr. Marianne Aznar

July 2nd (Sat): Fairness & Bias in Medical AI: Deep Learning / Image Analysis Techniques in PET / MR Imaging and its Challenges

Speaker: Dr. Udunna Anazoddu

Workshops

July 3rd (Sun): Deep Learning Framework (PyTorch): Model Training and Evaluation

Instructor: Dr. Mohammad Reza Mehebbian

July 6th (Wed): Deep Learning Framework (PyTorch): Implement your own Image Classifier

Instructor: Dr. Mohammad Reza Mehebbian

Week 5: Fully Supervised Medical Image Segmentation

Seminars

July 16th (Sat): Implementation of Deep Learning Software from an Industry Perspective

Speakers: Dr. Karl Otto, Dr. Joshua Giambattista, Carter Kolbeck and Jon Giambattista (Limbus AI)

July 16th (Sat): Implementation of Deep Learning Software from a Regulatory Perspective

Speaker: Dr. Andreu Badal

What is the FDA approval process when deciding whether or not to commercialize a research product?

Workshops

July 17th (Sun): Fully Supervised Medical Image Segmentation

Instructor: Hunter Buckhorn

July 20th (Wed): Deep Learning Framework (PyTorch): Tensorboard

Instructors: Sébastien Quetin and Yujing Zou

Week 6: Deep Learning Framework (PyTorch): Implement U-Net

Seminars

July 23rd (Sat): Decreasing Annotation Labour for Digital Pathology Segmentation and Downstream Outcome Prediction Models

Speaker: Dr. Anne Martel

Bottlenecks in digital pathology whole-slide-image (WSI) deep learning models and weakly supervised learning techniques, biomarkers extracted from WSI used in clinical decision support system.

July 23rd (Sat): Instance Segmentation on Colonoscopy Images

Speaker: To be confirmed

Workshops

July 24th (Sun): Deep Learning Framework (PyTorch): Implement U-Net

Instructor: Dr. Mohammad Reza Mehebbian

July 27th (Wed): Deep Learning Model Generalizability: Pitfalls and Best Practices

Instructor: Dr. Katie Ovens

Week 7: Supervised, Weakly Supervised, Semi-Supervised, and Unsupervised Learning

Seminars

July 30th (Sat): Clinical Decision Support Systems: Distributed Learning for Radiomics and Outcome Prediction

Speaker: To be confirmed

Creating generalizable models while keeping patient privacy in mind. Multi-institutional studies using Federated Learning. An introduction to the precision-medicine-toolbox. What to do with small datasets across institutions?

July 30th (Sat): Clinical Decision Support Systems: Use of Multimodality Imaging in AI and Outcome Prediction

Speaker: To be confirmed

State-of-the-art models based on multimodality images, generalizable ground-truths in segmentations, and fusion methods among multiple medical imaging modalities.

Workshops

July 31st (Sun): Weakly Supervised, Semi-Supervised, and Unsupervised Learning

Instructors: Dr. Farhad Maleki and Hossein Jafarzadeh

August 3rd (Wed): Radiomics and Treatment Outcome Prediction

Instructor: Dr. Ibrahim Chamseddine

Week 8: Closing ceremony
August 6th (Sat): Closing Ceremony

Speakers: Dr. Sylvain Baillet and Dr. Shirin Abbasinejad Enger

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

This workshop series and hackathon would not be possible without the generous support of our sponsors. Thank you!!