Meet Dr. Karl Otto

Karl is an independent researcher/entrepreneur holding an Adjunct Professorship in the Department of Physics and Astronomy at the University of British Columbia.  He received Bachelors and Masters degrees in physics/medical physics at McGill before completing a PhD at UBC in 2003 while working part time at BC Cancer as a clinical physicist. Focusing on potentially high impact near term clinical applications, he continued to explore research opportunities including Volumetric Modulated Arc Therapy (VMAT), 4D VMAT planning and real-time interactive planning methods for general and adaptive RT.  Working with Varian Medical Systems, the planning algorithms developed by Dr. Otto became the basis for the commercial RapidArc system which is now used in more than a thousand cancer centers globally.  More recently Dr Otto joined Limbus AI to pursue applications of deep learning and AI in radiation therapy.

You can get in touch with Dr. Otto on LinkedIn.

Meet Dr. Renata Raidou

We are beyond excited to have Dr. Renata Raidou present about “Visual Analytics for Cancer Radiotherapy” as a keynote speaker during our McMedHacks event on June 19th 10:00-11:30 EDT.

Renata Raidou is Assistant Professor (Tenure Track) in Medical Visualization and Visual Analytics at TU Wien, Austria. She was previously employed as Assistant Professor (Tenure Track Rosalind Franklin Fellow) at the University of Groningen, the Netherlands. She did her Post-Doc at TU Wien, and she received her Ph.D. from Eindhoven University of Technology, the Netherlands, in 2017. The topic of her dissertation was “Visual Analytics for Digital Radiotherapy: Towards a Comprehensible Pipeline”, and for the results of her work, she obtained the Best Ph.D. Award 2018 of the EuroVis Awards Programme. Additionally, she was awarded the Dirk Bartz Prize for Visual Computing in Medicine (1st Place) at Eurographics 2017. Her research focus is on the interface between Visual Analytics, Image Processing, and Machine Learning, with a strong focus on medical applications—in particular, cancer radiotherapy. For more info: https://renataraidou.com/

Meet Dr. Christina Gillmann

We are very excited to have Dr. Christina Gillmann present about “Multi-modal Machine Learning for CT – From an example to Generality” at our McMedHacks event on July 3rd at 10:00-11:30 EST. Christina is a scientist at Leipzig University discovering the 
exciting edge between machine learning and medicine.

You can learn more about Dr. Gillmann on her scholar website and her research gate website.

Meet Sachin Dev

Hello, my name is Sachin Dev. I am a part of McMedHack 2021 social media and sponsorship team. I have been working in Medical Physics since 2014. I was a clinical Medical Physicist in India and later joined Open Health Systems Laboratory (OHSL). At OHSL, I was responsible for scientifically coordinating and initiating projects in the radiation oncology area. I was involved with OHSL major initiatives in Radiobiology, Monte Carlo acceleration, and Global Cancer Research Network. Working with OHSL, I came up with the idea of acceleration of Monte Carlo simulations based on hardware technology and proposed the use of a yet untested hardware solution – Field Programmable Gate Array (FPGA). I am currently a PhD student in Dr. Shirin Enger lab, Medical Physics Unit, McGill University where I am working on developing radiobiological models based on patient-specific microdosimetric data. The other areas of my research interests include particle therapy, biological treatment planning and science gateways. I aim to make cancer treatment cheap, globally available and more accurate.
Get in touch with me on LinkedIn or Twitter.

Meet Dr. Ibrahim Chamseddine

We are very excited to have Dr. Ibrahim Chamseddine as an instructor for our McMedHacks workshop about multimodal image analysis and treatment outcome prediction. Ibrahim Chamseddine is a Postdoctoral Fellow at Harvard Medical School and at the Department of Radiation Oncology of Massachusetts General Hospital. He received his Ph.D. in Mechanical Engineering from McGill University in 2019, after which he was a postdoctoral researcher in the Department of Mathematical Oncology at Moffitt Cancer Center. His research focuses on cancer treatments outcome prediction, optimization, and personalization.

You can get in touch with Dr. Chamseddine on LinkedIn and Twitter @CancerOptim.

Meet Dr. Shirin Abbasinejad Enger

We are very excited to have Dr. Shirin Abbasinejad Enger as a keynote speaker for our McMedHacks series. She will give an introduction to McMedHacks, our team and our mission in the coming weeks on June 12th at 14:00-15:30 EST.

Dr. Shirin Abbasinejad Enger is Associate Professor at the Gerald Bronfman Department of Oncology, McGill University, and Canada Research Chair in Medical Physics. She is research director of Medical Physics at the Lady Davis Institute for Medical Research and Segal Cancer Centre of the Jewish General Hospital.

You can learn more about Dr. Enger on her LinedIn and Twitter @DrShirinAEnger.

Meet Dr. Roy Keyes

We are very excited to have Dr. Roy Keyes as an invited speaker for our McMedHacks series. He will present about the past, present, and future of machine learning in medical imaging and related applications on June 19th at 10:00-11:30 EST.

Dr. Roy Keyes has worked professionally in data science and machine learning since 2012. His experience has crossed many domains, primarily in the tech world. His most recent focus has been on building and leading machine learning and data science teams. Prior to that he trained in medical physics, with a focus on computational methods for radiation therapy.

To learn more about visit roycoding.com and https://twitter.com/roycoding.

Meet Ximeng Mao

Role: Instructor for the introduction to deep learning workshop

Hello, my name is Ximeng Mao, and I am a computer science PhD student at Mila and University of Montreal. Before that, I did my Master’s at McGill computer science while working at the medical physics unit. I have wide interests in Deep Learning and its applications in medical domains. Previously, I have worked in developing RapidbrachyDL, a deep convolutional neural network to predict the Monte-Carlo dose maps, and my other works involve the use of generative model to infer disentangled features from medical images, as well as the use of reinforcement learning to search for the optimal treatment plans. I am currently working in the intersection of deep learning and neurosciences, especially on the topic of the brain-computer interface.

You can get in touch with me on LinkedIn: https://www.linkedin.com/in/ximeng-mao-54b19726/

McMedHacks Registration Just Opened!

Now is your chance to register for a free workshop series about medical image analysis and deep learning in Python called McMedHacks. You will meet experts in the field and acquire all the tools you need to kickstart your research projects. The interactive workshop series is beginner friendly and will teach everything from the fundamentals of medical image analysis to some of the most advanced deep learning architectures used in cutting-edge research today.

To register for the event, please fill out this Google Form: https://forms.gle/qu8hedJ8R7fXt4XA7

(Note: If you have filled out a Google Form for this event prior to May 21st that form was used to gauge interest, not for registration. Please fill out the above form to register.)

We will be hosting the workshops weekly from June 14th to July 31st. Because we cannot predict COVID regulations, the workshops and will be held online. We hope to accommodate your schedules, so we would like to know your availability for the weekly presentations and the weekly interactive workshops. Please fill out the when2meet here or in the registration form to let us know your availability. (please make sure to use the correct time zone)

We will record the workshop series but would highly encourage you to attend as you can follow along in real-time and ask questions.

The workshop series will consist of two weekly events:

1) A key-note speaker will present their research in the field of AI and medical imaging, including a time for you to ask questions about ongoing research.

2) A workshop about medical image analysis and deep learning that will include an interactive portion, where you will be able to code alongside us using a Google Colab notebook. Our first workshop will walk you through how to use all the necessary tools, so you need no prior knowledge.

If you would like to learn more about our event, visit our Facebook, LinkedIn, and Twitter. You will also get the chance to put your skills to the test at the McMedHacks hackathon that will take place in early August. Follow us on social media to stay up to date as we release more information.

Best,
The McMedHacks Team
Yujing Zou
Luca Weishaupt

Luca Weishaupt

Luca Weishaupt

Luca Weishaupt

Undergraduate
B.Sc. Physics & Computer Science
Artificial Intelligence Group
+1 (914) 486-4460
Learn more

Bio

Luca was born and raised in Germany and completed a DIAB (German Abitur and high school diploma) at the German International School New York after moving to the United States. He is in the last year of his undergraduate degree with a Major in Physics and a Minor in Computer Science at McGill University. Luca joined Enger Lab in the first year of his undergraduate studies in 2018 and has been working on deep learning-based medical image analysis and treatment planning optimization.

Current Projects

Luca is part of the Enger Lab Artificial Intelligence group.

Multimodal Treatment Outcome Prediciton

Luca is developing a deep learning-based, multi-modal treatment outcome prediction model for endorectal cancer patients. The project combines radiomics with deep learning to increase prediction accuracy, increase efficiency, and eliminate human error to enable patient-specific treatment selection.

Tumor segmentation in endoscopy images

Luca is investigating the inter-observer variability of the manual segmentation of tumor regions in endoscopy images and its effect on treatment outcomes. Furthermore, Luca is developing a deep learning-based segmentation tool that can learn from multiple observers labels with high inter-observer variability.

McMedHacks – Medical Image Analysis and Deep Learning in Python

Luca is the co-director and founder of McMedHacks, which is an 8-week educational program about medical image analysis and deep learning in Python. McMedHacks consists of presentations from leaders in industry and academia, workshops from researchers in deep learning-based medical image analysis, and a hackathon. McMedHacks 2021 has 365 registered participants from 38 countries ranging from undergraduates to PhDs and MDs. The growing McMedHacks team consists of students from Enger Lab as well as collaborators from around the world with more than 30 members.

Publications

Sorry, no publications matched your criteria.