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Abstract

Purpose/Objective: The McMedHacks workshop and presentation series was created to teach individuals from various backgrounds about deep learning (DL) for medical image analysis in May, 2021. Material/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 the taught materials 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.
S4 Abstract book ESTRO 2022
Figure 1.
A clear reduction in inter-individual contouring variability was observed when comparing the delineations of the participants
during the first training session (m1) and the second one (m2), as illustrated for the corpus callosum, amygdala, and
thalamus.
Conclusion
Explanatory movies were developed with the aim of reducing inter- and intra-individual variability in the delineation of the
10 OARs introduced in the updated EPTN atlas. These movies will be made available on www.cancerdata.org.
SP-0014 McMedHacks: Deep learning for medical image analysis workshops and Hackathon in radiation oncology
Y. Zou1, L. Weishaupt2, S. 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
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.
Materials 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.
S5 Abstract book ESTRO 2022
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.
Debate: This house believes that FLASH radiotherapy is a more promising avenue for the future of radiation oncology
than particle radiotherapy
SP-0017 For the motion (FLASH: Increasing the therapeutic index of radiotherapy)
ResearchGate has not been able to resolve any citations for this publication.
ResearchGate has not been able to resolve any references for this publication.