Hi everyone! My name is Wendi Qu and I’m finishing my third year in U of T, majoring in Statistics and Molecular Genetics. I did a ROP399 research project with Dr. Pascal Tyrrell from September 2018 – April 2019 and I would love to share it with you!
his amazing experience, and it has inspired me to delve deeper into machine learning and healthcare!
and I was inclined to put my minor to the test! Little did I know that I was about to embark on a machine learning adventure.
wasn’t going to be a part of the lab for 2018-2019 year. If my background in statistics has taught me anything, nothing truly has a 100% probability. And yet, last April I found myself sitting in the department of medical imaging at my first lab meeting.
should add an investigation of k-fold cross validation because the majority of models use this to validate their estimate of model accuracy. With further help from Ariana’s colleague, Mauro, I was able to gather a ton of data so that I could analyze my results statistically.
costs of its employment, I showed that L2 regularization is a feasible procedure to help prevent over-fitting and improve testing accuracy when developing a machine learning model with limited training data.
machine learning model. I harnessed powerful technologies like Intel AVX2 vectorization instruction set for things like image pre-processing on the CPU and the Nvidia CUDA runtime environment through PyTorch to accelerate tensor operations using multiple GPUs. Overall, the final run of my experiment took about 25 hours to run even with all the high-level optimizations I considered—even on an insane lab machine with an Intel i7-8700 CPU and an Nvidia GeForce GTX Titan X!
X-ray artifacts. And now, looking back, it was one of the best learning experiences I have ever had, through an enormous amount of self-teaching, practicing, troubleshooting, discussing and debating. As with all learning experiences, the process can be long and bewildering, sometimes even tedious; yet rewarding in the end.
office, waiting for him to print out my ROP application and start off the interview. At that point, I just ended my one-year research at a plant lab and was clueless of what I was going to do for the following summer. Coming from a life science background, I went into this interview for a machine learning project in medical imaging knowing that I wasn’t the most competitive candidate nor the most suitable person to do the job. Although I tried presenting myself as someone who had had some experience dealing with statistics by showing Dr. Tyrrell some clumsy work I did for my previous lab, the flaws were immediately noticed by him. I then found myself facing a series of questions which I had no answers to and the interview quickly turned into what I thought to be a disaster for me. I was therefore very shocked when I received an email a week later from Dr. Tyrrell informing me that I had been accepted. I happily went onboard, but joys aside, part of me also had this big uncertainty and doubt that later followed me even to my first few weeks at the lab.
gradually went away. But it was never going to be easy. There were times when
we hit the bottleneck; when our attempts have failed miserably; when we had to give up on a brilliant idea because it didn’t go our ways. But after stumbling through all the challenges and pitfalls, we found ourselves new. I was a bit lost at the beginning of this summer. But over the summer I learned a lot about the very cool and growingly crucial field of machine learning; I grew a newfound appreciation for statistics and methodology; I picked up the programming language python, which I had been wanting to do for years and, most importantly, I did more thinking than I ever would if I were to just follow instructions blindly. And in the end, I believe that science is all about thinking. So for you guys out there reading the blog, if you’re coming to this lab from a totally different background and not entirely sure about the future, don’t be afraid. And I hope you find what you come here looking for, just like I did.
My name is Samantha Santoro, and I am completing my second year in the English and Biology majors at the University of Toronto, St. George. A rather unconventional combination, when reviewing past students of Dr. Tyrrell’s lab. I was a 2017-2018 Research Opportunity Program (ROP) student in Dr. Pascal Tyrrell’s lab, and my work chiefly consisted of evaluating the internal vessel wall volumes of carotid arteries in a particular cohort of patients provided by the ongoing prospective CAIN study. My ROP was in the field of Medical Imaging. I am the co-president of the student club known as Watsi, with the main chapter based in San Francisco. I am also a special contributor to the Rare Disease Review, along with volunteering at an amalgamation of charity walks and fundraisers.
My ROP project was a turbulent experience – although that word is typically associated with a negative connotation, I regard my ROP299Y1 as one of the most humbling, interesting, and educative experiences that I have had thus far – most definitely not negative. However, to say everything went smoothly would be discrediting the lessons I learned from when things were not idyllic and smooth. My project, as aforementioned, statistically analyzed data provided by patients part of the CAIN study (an analysis that could not have existed without Dr. Tyrrell’s generous and unwavering support). My study determined that patients who were found to have IPH, or what is known as intraplaque hemorrhage, when I analyzed their MRIs, were also found to have increased vessel wall volume. This conclusion is incredibly significant, as IPH is a surrogate marker for atherosclerosis and could potentially be an indicator for patients at risk of future cerebrovascular events (namely, ischemic stroke). As strokes are currently the number three killer in the U.S and Canada alone, and heart disease number one, having a potential indicator for patients at risk of stroke would greatly benefit clinicians in their practice, as well as patients themselves.
As aforementioned, studies similar to my own are currently underway by the Canadian Atherosclerosis Imaging Network, furthering the important research in this field. The VBIRG (Vascular Biology Imaging Research Group) was the lab in which I primarily worked throughout the course of my ROP, at Sunnybrook Hospital. Moreover, I also worked on systematic reviews and reports outside of the focus of my project, in the fields of medical ethics and AI in the radiology workplace – both of which were opportunities provided to me by Dr. Tyrrell, and both of which were incredibly valuable experiences, allowing for me to broaden my knowledge of certain areas of medicine and science that are developing and expanding.
Although my project was littered with its own respective difficulties – a substantial number of drafts throughout each step of the program (more than I had ever made, even being an English student); a reluctant, but later fulfilling, acquaintanceship with the post-processing software VesselMass; and several late nights learning about the field of statistics – it is in light of these difficulties, and at present having overcome them throughout my ROP, that I remember Dr. Paul Kalanithi’s words in his memoir When Breath Becomes Air: “It occurred to me that my relationship with statistics changed as soon as I became one”. He, too, had studied Biology and English. I may not have played a lead role in the statistics I had been working with, but I can now say that understanding what they meant and how they were formulated has generated a deep respect in me for the field of statistics.
My poster was on display at the 2018 Research Opportunity Undergraduate Fair. Special thanks to Mariam Afshin, my supervisor at Sunnybrook Hospital; Bowen Zhang, for answering each question I had while at Sunnybrook; John, and the rest of the lab team; and Dr. Pascal Tyrrell, for answering my email last February and holding my interview on the same day as my Chemistry exam. Never before had I met such an – in a word – outstanding professor, and I dare say that I will never meet one like him throughout the rest of my academic journey.
Well, I think it was inevitable. My data science lab has slowly crossed over to the dark side into the world of Machine Learning and Artificial Intelligence.
Let me apologize for being MIA for so long. Life has been pretty hectic these past months as I have been building the MiDATA program here in the Department of Medical Imaging at the University of Toronto. The good news is that the MiVIP program will now be inviting students to participate in machine learning and artificial intelligence in medical image research.
This summer will include the launch our our MiStats+ML program where we will have students from the department of statistical sciences, computer sciences, and life sciences all work together on ML/AI projects in the MiDATA lab.
Stay tuned as we ramp up and get back to some our previous threads like MiWORD of the day…
See you in the blogosphere,