McMedHacks: Deep learning for medical image analysis workshops and Hackathon in radiation oncology
OC-0014
Abstract
McMedHacks: Deep learning for medical image analysis workshops and Hackathon in radiation oncology
Authors: Yujing Zou1, Luca Weishaupt2, Shirin Enger3
1McGill University, Department of Oncology, Medical Physics Unit, Montreal, Canada; 2McGill University, Department of Physics , Montreal, Canada; 3McGill University , Gerald Bronfman Department of Oncology, Research Director, Translational Physics and Radiobiology at The Lady Davis Institute and Segal Cancer Centre of the Jewish General Hospital, Montreal, Canada
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Purpose or Objective
The McMedHacks workshop and presentation series was created to
teach individuals from various backgrounds about deep learning (DL) for medical
image analysis.
Material and Methods
McMedHacks is a free and student-led 8-week summer program.
Registration for the event was open to everyone, including a form to survey
participants’ area of expertise, country of origin, level of study, and level
of programming skills.
The weekly workshops were instructed by 8 students and experts
assisted by 20 mentors who provided weekly tutorials. Recent developments in DL
and medical physics were highlighted by 21 leaders from industry and academia.
A virtual grand challenge Hackathon took place at the end of the workshop
series.
All events were held virtually and recorded on Zoom to accommodate
all time zones and locations. The workshops were designed as interactive coding
demos and shared through Google Colab notebooks.
Results
McMedHacks gained 356 registrations from participants of 38
different countries (Fig. 1) 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 (Fig. 2).
Sixty-nine participants earned a certificate of completion by having engaged
with at least 12 of all 14 events. The program received 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. The eight-week long workshop’s duration allowed
participants to digest materials taught in a continuous manner as opposed
to bootcamp-style conference workshops. 
Conclusion
The overwhelming interest and engagement for the McMedHacks
workshop series from the Radiation Oncology (RadOnc) community illustrates a
demand for Artificial Intelligence (AI) education in RadOnc. The future of
RadOnc clinics will inevitably integrate AI. Therefore, current RadOnc
professionals, and student and resident trainees should be prepared to
understand basic AI principles and its applications to troubleshoot, innovate,
and collaborate.
McMedHacks set an excellent example of promoting open and
multidisciplinary education, scientific communication, and leadership for
integrating AI education into the RadOnc community on an international level.
Therefore, we advocate for implementation of AI curriculums in professional
education programs such as Commission on Accreditation of Medical Physics
Education Programs (CAMPEP). Furthermore, we encourage experts from around the
world in the field of AI, or RadOnc, or both, to take initiatives like
McMedHacks to collaborate and push forward AI education in their departments
and lead practical workshops, regardless of their levels of education.