Jenny Du’s ROP299 Journey: Telling apart the real and the fake!

My name is Jenny Du, and I have just wrapped up my ROP299 project in the Tyrrell Lab, as well as my second year at the University of Toronto, pursuing a bioinformatics specialist. Looking back, it was a bumpy ride, but in the end, this journey was very rewarding and has taught me a lot of things on both machine learning topics as well as the process of scientific research.

Like most of the other ROP299 students, I had no experience with machine learning and neural networks. Despite doing some research beforehand, I found myself googling what everyone was talking about during the weekly meetings (thankfully, they were online) to make sure I was not completely lost. None of my first-year courses had prepared me for these kinds of things! And so, with some uncertainties in my heart, I started my ROP journey.

I decided on my overall research topic fairly early, but the details were adjusted several times as I progressed through my project. My project is about coming up with a way to quantitatively assess a set of synthetic ultrasound images in terms of how “realistic” they look compared to the real ultrasound images. “Realism” here is defined as whether the synthetic images can be used as training images in replacement of the real images without creating too big of an impact on the machine learning algorithm. At first, I came up with a naïve proposal: I will build an algorithm that differentiates real and synthetic ultrasound images, and if the algorithm can classify the two kinds (with high accuracies), then it means that the synthetic images are not realistic, and vice versa. In the weekly meeting, Dr. Tyrrell immediately pointed out why this wouldn’t work. In my proposal, a low accuracy could mean that the synthetic and the real images are very similar, but it could also mean that the algorithm itself is terrible. For example, if my algorithm has 50% accuracy, then it is basically randomly guessing each image, like a coin toss, so its classification is unreliable, to say the least. He suggested that I look online to see how others have done it. There was very little information that directly relates to what I’m doing, but eventually I was able to come up with a plan to extract features from the images using a pre-trained CNN model and measure the cosine similarity score between two images and graph these values into a histogram to see their distribution. Dr. Tyrrell also suggested that I compare the distributions at different equivalence margins to determine how big a mean difference is acceptable.

Thankfully, I was able to find some code online that I was able to use in my project with minor changes, and I was able to produce some distribution data fairly quickly. Then, I encountered what I considered to be the hardest part of my entire project: to statistically interpret and discuss my data and create a conclusion out of it. Since I am not a statistics student, and so my knowledge of statistics is limited to one stats course I took as a part of my program requirements. It took a while for me to learn all these statistical concepts and understand why each is needed in my project.

This year was especially interesting since everything was online. Despite not being able to see each other face-to-face, I was still able to receive much support from Dr. Tyrrell and other students in the lab. Mauro was very helpful in preparing the datasets for my project as well as answering any problems related to the codes. Guan also helped to check my statistical calculations and clarifying some hard concepts. I have also made great friends with the other ROP students this year, and hopefully we will be able to see each other in person when the school re-opens.

Overall, this journey was a wonderful experience, and I have learned many things from it. Not only did I got some familiarity with machine learning topics and their application in medicine, but I have also gained experience in the general academic research process, from coming up with a topic to the actual implementation to the final reports. There were challenges along the way, but in the end, it was very rewarding. I am extremely thankful to Dr. Tyrrell for the guidance and support and am grateful for this opportunity.

Jenny Du

Parinita Edke’s ROP experience in the Tyrrell Lab!

Hi! My name is Parinita Edke and I’m finishing my third year at UofT, specializing in Computer Science with a minor in Statistics. I did a STA399Y research project with Professor Tyrrell from September 2020 – April 2021 and I am excited to share my experience in the lab!

I have always been interested in medicine and the applications of Computer Science and Statistics to solve problems in the medical field. I was looking out for opportunities to do research in this intersection and was excited when I saw Professor Tyrrell’s ROP posting. I applied prior to the second-round deadline and waited to hear back. After almost 2 weeks past the deadline, I had still not heard back and decided to follow up on the status of my application. I quickly received a reply from Professor Tyrrell that he had already picked his students prior to receiving my application. While this was extremely disappointing, I thanked Professor Tyrrell for his time and expressed that I was still interested in working with him during the year and attached my application package to the email. I was not really expecting anything coming out of this, so I was extremely happy when I received an invite to an interview! After a quick chat with Professor Tyrrell about my goals and fit for the lab, I was accepted as an ROP student!

Soon after being accepted, I joined my first lab meeting where I was quickly lost in the technical machine learning terms, the statistical concepts and the medical imaging terminology used. I ended the meeting determined to really begin understanding what machine learning was all about!

This marked the beginning of the long and challenging journey through my project. When I decided on my project, it seemed interesting as solving the problem allowed for some cool questions to be answered. The task was to detect the presence of blood in ultrasound images of the knee joint; my project was to determine if Fourier Transformation can be used to generate features to perform the task at hand well. It seemed quite straightforward at first – simply generate Fourier Transformed features and run a classification model to get the outputs, right? After completing the project, I am here to tell you that it was far from being straightforward. It was more like a zigzag progress pattern through the project. The first challenge that I faced was understanding the theory behind the Fourier Transform and how it applies to the task at hand. This took me quite some time to fully grasp and was definitely one of the more challenging parts of the project. The next challenge was figuring out the steps and the things I would need for my project. Rajshree, a previous lab member, had done some initial work using a CNN+SVM model. I first tried to replicate what Rajshree had done in order to create a baseline to compare my approach to. It took me some time to understand what each line of code did within Rajshree’s model but after I was able to get it to work, I felt amazing! Reading through Rajshree’s code gave me more experience in understanding the common Python libraries used in machine learning, so when I built my model, it was much quicker! When I ran my model for the first time, I felt incredible! The process was incredibly frustrating at times, but when I saw results for the first time, I felt like all this struggle was worth it. Throughout this process of figuring out the project steps and building the model, Mauro was always there to help, always being enthusiastic when answering any questions I had and giving me encouragement to keep going.

Throughout the process, Professor Tyrrell was always there as well – during our weekly ROP meetings, he always reminded us to think about the big picture of what our projects were about and the objectives we were trying to accomplish. I definitely veered off in the wrong direction at times, but Professor Tyrrell was quick to pull me back and redirect me in the right direction. Without this guidance, I would not have been able to finish and execute the project in the way that I did and am proud of.

Looking back at the year, I am astonished at the number of things I have learned and how much I have grown. Everything that I learned, not only about machine learning, but about writing a research paper, learning from others and your own mistakes, collaborating with others, learning from even more of my own mistakes, and persevering when things get tough will carry with me throughout the rest of my undergraduate studies and the rest of my professional career.

Thank you, Professor Tyrrell, for taking a chance on me. He could have simply passed on my application but the fact that he took a chance with me and accepted me into the course lead to such an invaluable experience for me which I truly appreciate. The experiences and the connections I have made in this lab have been a highlight of my year, and I hope to keep contributing to the lab in the future!

Parinita Edke

Stanley Hua in ROP299: Joining the Tyrrell Lab during a Pandemic

My name is Stanley Hua, and I’ve just finished my 2nd year in the bioinformatics program. I have also just wrapped up my ROP299 with Professor Pascal. Though I have yet to see his face outside of my monitor screen, I cannot begin to express how grateful I am for the time I’ve been spending at the lab. I remember very clearly the first question he asked me during my interview: “Why should I even listen to you?” Frankly, I had no good answer, and I thought that the meeting didn’t go as well as I’d hoped. Nevertheless, he gave me a chance, and everything began from there.

Initially, I got involved with quality assessment of Multiple Sclerosis and Vasculitis 3D MRI images along with Jason and Amar. Here, I got introduced to the many things Dmitrii can complain about taking brain MRI images. Things such as scanner bias, artifacts, types of imaging modalities and prevalence of disease play a role in how we can leverage these medical images in training predictive models.

My actual ROP, however, revolved around a niche topic in Mauro and Amar’s project. Their project sought to understand the effect of dataset heterogeneity in training Convolutional Neural Networks (CNN) by cluster analysis of CNN-extracted image features. Upon extraction of image features using a trained CNN, we end up with high-dimensional vectors representing each image. As a preprocessing step, the dimensionality of the features is reduced by transformation via Principal Component Analysis, then selecting a number of principal components (PC) to keep (e.g. 10 PCs). The question must then be asked: How many principal components should we use in their methodology? Though it’s a very simple question, I took way too many detours to answer this question. I looked at the difference between standardization vs. no standardization before PCA, nonlinear dimensionality reduction techniques (e.g. autoencoder) and comparisons of neural network image representation (via SVCCA) among other things. Finally, I proposed an equally simple method for determining the number of PCs to use in this context, which is the minimum number of PCs that gives the most frequent resulting value (from the original methodology).

Regardless of the difficulty of the question I sought to answer, I learned more about practices in research, and I even learned about how research and industry intermingle. I only have Professor Pascal to thank for always explaining things in a way that a dummy such as me would understand. Moreover, Professor Pascal always focused on impact; is what you’re doing meaningful and what are its applications?

 I believe that the time I spent with the lab has been worthwhile. It was also here that I discovered that my passion to pursue data science trumps my passion to pursue medical school (big thanks to Jason, Indranil and Amar for breaking my dreams). Currently, I look towards a future, where I can drive impact with data; maybe even in the field of personalized medicine or computational biology. Whoever is reading this, feel free to reach out! Hopefully, I’ll be the next Elon Musk by then…

Transiently signing out,

Stanley Bryan Z. Hua

Jessica Xu’s Journey in ROP299

Hello everyone! My name is Jessica Xu, and I’ve just completed my second year in Biochemistry and Statistics at the University of Toronto. This past school year, I’ve had the wonderful opportunity to do a ROP299 project with Dr. Pascal Tyrrell and I’d like to share my experience with you all!

A bit about myself first: in high school, I was always interested in life sciences. My favourite courses were biology and chemistry, and I was certain that I would go to medical school and become a doctor. But when I took my first stats course in first year, I really enjoyed it and I started to become interested in the role of statistics in life sciences. Thus, at the end of my first year, while I was looking through the various ROP courses, I felt that Dr. Tyrrell’s lab was the perfect opportunity to explore my budding interest in this area. I was very fortunate to have an interview with Dr. Tyrrell, and even more fortunate to be offered a position in his lab!

Though it may be obvious, doing a research project when you have no research experience is very challenging! Coming into this lab having taken a statistics course and a few computer science courses in first year, I felt I had a pretty good amount of background knowledge. But as I joined my first lab meeting, I realized I couldn’t be more wrong! Almost every other word being said was a word I’d never heard of before! And so, I realized that there was a lot I needed to learn before I could even begin my project.

I then began on the journey of my project, which was looking at how two dimension reduction techniques, LASSO and SES, performed in an ill-posed problem. It was definitely no easy task! While I had learned a little bit about dimension reduction in my statistics class, I still had a lot to learn about the specific techniques, their applications in medical imaging, and ill-posed problems. I was also very inexperienced in coding, and had to learn a lot of R on my own, and become familiar with the different packages that I would have to use. It was a very tumultuous journey, and I spent a lot of time just trying to get my code to work. Luckily, with help from Amar, I was able to figure out some of the errors and issues I was facing in regards to the code.

I learned a lot about statistics and dimension reduction in this ROP, more than I have learned in any other courses! But most importantly, I had learned a lot about the scientific process and the experience of writing a research paper. If I can provide any advice based on my experience, it’s that sometimes it’s okay to feel lost! It’s not expected of you to have devised a perfect plan of execution for your research, especially when it’s your first time! There will be times that you’ll stray off course (as I often did), but the most valuable lesson that I learned in this ROP is how to get back on track. Sometimes you just need to take a step back, go back to the beginning and think about the purpose of your project and what it is you’re trying to tell people. But it’s not always as easy to realize this. Luckily Dr. Tyrrell has always been there to guide us throughout our projects and to make sure we stay on track by reminding us of the goal of our research. I’m incredibly grateful for all the support, guidance, and time that Dr. Tyrrell has given this past year. It has been an absolute pleasure of having the experience of working in this lab.

Now that I’ve taken my first step into the world of research, with all the new skills and lessons I’ve learned in my ROP, I look forward to all the opportunities and the journey ahead!

Jessica Xu

Jacky Wang’s ROP399 Journey

My name is Jacky Wang, and I am just finishing my third year at the University of Toronto, pursuing a computer science specialist. Looking back on this challenging but incredible year, I was honoured to have the opportunity to work inside Dr. Tyrrell’s lab as part of the ROP399 course. I would love to share my experience studying and working inside the lab.

Looking back, I realize one of the most challenging tasks is getting onboard. I felt a little lost at first when surrounded by loads of new information and technologies that I had little experience with before. Though feeling excited by all the collision of ideas during each meeting, having too many choices sometimes could be overwhelming. Luckily after doing more literature review and with the help of the brilliant researchers in the lab (a big thank you to Mauro, Dimitri, and of course, Dr. Tyrrell), I start to get a better view of the trajectories of each potential project and further determine what to get out from this experience. I did not choose the machine learning projects, though they were looking shiny and promising as always (as a matter of fact, they turned out to be successful indeed). Instead, I was more leaning towards studying the sample size determination methodology, especially the concept of ill-posed problems, which often occur when the researchers make conclusions from models trained on limited samples. It had always been a mystery why I would get different and even contrasting results when replicating someone else’s work on smaller sample sizes. From there, I settled the research topic and moved onto the implementation details.

This year the ROP students are coming from statistics, computer science and biology etc. I am grateful that Dr. Tyrrell is willing to give anyone who has the determination to study in his lab a chance though they may have little research experience and come from various backgrounds. As someone who studies computer science with a limited statistics background, the real challenge lies in understanding all the statistical concepts and designing the experiments. We decided to apply various dimension reduction techniques to study the effect of different sample sizes with many features. I designed experiments around the principal component analysis (PCA) technique while another ROP student Jessica explored the lasso and SES model in the meantime. It was for sure a long and memorable experience with many debugging when implementing the code from scratch. But it was never more rewarding than seeing the successful completion of the code and the promising results.

I feel lucky and grateful that Dr. Tyrell helped me complete my first research project. He broke down the long and challenging research task into clear and achievable subgoals within our reach. After completing each subgoal, I could not even believe it sent us close to the finished line. It felt so different taking an ROP course than attending the regular lessons. For most university courses, most topics are already determined, and the materials are almost spoon-fed to you. But sometimes, I start to lose the excitement of learning new topics, as I am not driven by the curiosity nor the application needs but the pressure of being tested. However, taking the ROP course gives me almost complete control of my study. For ROP, I was the one who decides what topics to explore, how to design the experiment. I could immediately test my understanding and put everything I learned into real applications.

I am so proud of all the skills that I have picked up in the online lab during this unique but special ROP experience. I would like to thank Dr. Tyrrell for giving me this incredible study experience in his lab. There are so many resources out there to reach and so many excellent researchers to seek help from. I would also like to thank all members of the lab for patiently walking me through each challenge with their brilliant insights.

Jacky Wang

My name is Yiyun Gu and I am a fourth-year student studying mathematics and statistics at University of Toronto. After taking some statistical courses and machine learning courses, I was quite interested in applying machine learning methods and statistical methods to practice. Medical imaging is a popular field where machine learning methods have great impacts. Therefore, I contacted Dr. Pascal Tyrrell and he would like to supervise me.

Last September, my initial research direction was Bayesian optimization on hyperparameters of Convolutional Neural Networks based on the previous model information and the distributions. Besides Dr. Pascal Tyrrell’s instruction, he introduced his graduate student who was also interested in this field. We had weekly meetings to discuss how to make the idea implementable. I read many papers and learned relevant knowledge of Gaussian process, acquisition functions and surrogate functions. However, there was a huge challenge on how to update the hyperparameters of the prior distribution based on the information from the CNNs model. I was anxious about the progress. Dr. Pascal Tyrrell encouraged me to shift the direction a little bit because he cared about what a student learned and felt about the project.

Since November, out of interest in Bayesian concepts, I have been working on a project about comparing frequentist CNNs and Bayesian CNNs for the projects with sample size restrictions. Because there might not be sufficient data in medical imaging, I would like to determine whether Bayesian CNNs would benefit from prior information for small datasets and outperform frequentist CNNs. Bayesian CNNs update the distributions of weights and bias while frequentist CNNs use point estimates. The resources of the codes of Bayesian CNNs were limited. I tried to make full use of and modify the codes so that I could run the experiments from training sample size equal to 500 to training sample size equal to 50000. I applied customized architectures and AlexNet to MNIST and CIFAR-10 datasets. I found out that Bayesian CNNs didn’t perform well as I expected. Frequentist CNNs achieved higher accuracy and took less time compared to Bayesian CNNs. However, there is an interesting feature of Bayesian CNNs. Bayesian CNNs incorporate uncertainty measure. Since Bayesian CNNs have the distributions of weights, the models can also output the distributions of outputs. Therefore, Bayesian CNNs could tell how confident the decision is made.

I hope to apply more architectures of Bayesian CNNs to more datasets in medical imaging projects because architectures and datasets have great influences on the performance. Also, I would like to try more prior distributions and learn how to determine which distributions are more appropriate.

I had great research experience in this project with Dr. Pascal Tyrrell’s guidance and other graduate students’ help. It was my first time to write scientific report. Dr. Pascal Tyrrell kept instructing me how to write the report and offered great advice. I really appreciated the guidance and enjoyed the unique research experience in the end year of my undergraduate life. I look forward to contributing to medical imaging research and more opportunities to apply machine learning methods!

Yiyun Gu

Amar Dholakia: Some Thoughts as I Wrap Up My STA498Y Project (and Undergrad!)

Hi everyone! I’m Amar Dholakia and I’m a fourth-year/recent graduate having majored in Neuroscience and Statistics, and am starting a Masters’ in Biostatistics at UofT in the fall of 2020. I’ve had the pleasure of being a part of Dr. Tyrrell’s lab for almost two years now and would like to take the opportunity to reflect on my time here.

I started in Fall 2018 as a work-study student, tasked with managing the Department of Medical Imaging’s database. A highlight was discussing and learning about my peers’ work, which sparked my initial interest in the field of artificial intelligence and data science.

The following fall, I began a fourth-year project in statistics, STA498Y under the supervision of Dr. Tyrrell. My project investigated the viability of clustering of image features to assess dataset heterogeneity on deep convolutional network accuracy. Specifically, I compared the behaviour of six clustering algorithms to see if the choice of algorithm affected the ability to capture heterogeneity.

My project started out with reaching out to my labmate and good friend Mauro Mendez, who had recently undertaken a project very similar to mine. He sent me his paper, which I read, and re-read, and re-re-read… It took me about four months to only begin to grasp what Mauro had explored, and how I could use what he had learned to develop my project. But months of struggle was definitely worth the “a-ha!” moment.

First I started by replicating Mauro’s results using Fuzzy K as a clustering to make sure I was on the right track. Reading, coding, and testing the very first time was a nightmare – I had some Python experience but had never applied it before. It took a lot of back and forth with Mauro and Dr. Tyrrell , a lot of learning, understanding, and re-learning what I THOUGHT I understood to get me on the right track. By the start of the Winter term, I had finally conjured preliminary results – banging my head on the wall was slowly becoming worth it.

Once I had the code basics down, getting the rest of the results was relatively smooth sailing. I computed and plotted changes in model accuracy with sample size, and heterogeneity in model accuracy with sample size, as captured by different clustering methods. My results for one model were great from the get go – I was set! I thought to challenge myself by generalizing to a second model – and that was far from easy. But by taking that extra challenge, I felt I learned more about my project, and importantly, how to scientifically justify my results. The results didn’t match up, and I had to support my rationale with evidence (from the literature). If I couldn’t find an explanation, I may have done something incorrectly. And lo and behold, my ‘inexplicable’ results were in fact due to human error – something I very painstakingly troubleshooted, but now I understand much more and justify.

Ultimately, we showed that regardless of clustering technique, or CNN model, clustering could effectively detect how heterogeneity affected CNN accuracy. To me, this was an interesting result as I expected vastly different behaviour between partition-based and density-based

clustering. Nonetheless, it was welcome, as it suggested that any clustering method could be used to assess CNN.

I struggled most with truly appreciating what my research aimed to solve. I attribute this partially to not being as proactive with my readings and questions to Dr. Tyrrell to really verify my understanding. And to be honest, exploring this project is still a work-in-progress – something I will continue learning about this summer!

My advice to any future students – read, read, read! Diving into a specific academic niche is truly a wonderful experience. The learning curve was steep and initially involved a lot of trying, failing, fixing, and then trying again. But this experience only reinforced my notions of “success through failure” and “growth through struggle”. It may be challenging at first, but with some perseverance and support from a wonderful PI – like Dr. Tyrrell – you’ll be able to accomplish so much more than you originally imagined.

Sharing Medical Images for Research: Patients’ Perspectives

Michelle was our second YSP student this summer and did a great job at particpating in one of our studies in looking at patients’ willingness to share their medical images for research. This study is also part of the MiNE project.

Here is what Michelle had to say:

“My name is Michelle Cheung and I am a rising senior at Henry M. Gunn High School in Palo Alto, California. In my free time, I love to bake, read, travel with family, and take Barre classes. I also enjoy volunteering with friends at local charitable events and the Key Club at school. I am very interested in human biology and hope to study genetics and biotechnology next fall.

I really enjoyed the three weeks with the YSP Research Program. I learned so much about medical imaging modalities and had the amazing opportunity of helping research assistants survey patients at the Sunnybrook Hospital for the MiNE project. At first, it was a little daunting, but over time, I became more confident and comfortable interacting with patients, and grew to love surveying. The continuous surveying each day highlights the aspect and importance of repetition in conducting scientific research. Above all, it was an absolute pleasure getting to know the MiDATA and VBIRG lab. I’m grateful to my mentors and the lab members for exposing me to a whole new lab world I never thought existed beyond the traditional wet labs.”

Great job Michelle!

Have a peek at Michelle’s award winning poster and…

… I’ll see you in the blogosphere.

Pascal Tyrrell

Wow! What a Busy Summer….

Over 20 students in the lab this summer beavering away at some great projects. Last week my two Youth Summer Program (University of Toronto) students finished their three week stay with us. 

Jenny and Michelle both did fantastic work.

Today Jenny will show you her poster entitled:“Comparing Healthy and Unhealthy Carotid Arteries”

Jenny Joo is from Richmond Hill, Ontario, entering her senior year of high school. She plans on studying life science at the University of Toronto in the future. She spent the last 3 weeks in U of T’s YSP Medical Research program, where she was placed in two different medical imaging labs: The MiDATA lab of U of T and the Vascular Biology Imaging Research lab at Sunnybrook Hospital. 
Jenny chose to do research on the MRI scans of the carotid artery because it focused on both research and clinical aspects and had this to say about her experience with us: “It has been an enriching 3 weeks working with my PI, Pascal Tyrrell, my mentors, John Harvey and Moran Foster, and the rest of the research group.” 

Great work Jenny Joo!

Have a peek at her poster and…
… I’ll see you in the blogosphere.

Pascal Tyrrell

Dianna McAllister’s ROP Adventures in the Tyrrell Lab!

My name is Dianna McAllister and I am approaching the finish of my second year at University of Toronto, pursuing a bioinformatics specialist and computer science major. This year I was given the incredible opportunity to work in Dr. Tyrrell’s lab for the ROP299 course.
I have just handed in my first ever formal research paper for my work in Dr. Tyrrell’s lab. My project observed the effectiveness of using grad-CAM visualizations on different layers in a convolutional neural network. Though the end results of my project were colourful heat maps placed on top of images, the process to get there was not nearly as colourful or as effortless as the results may seem. There was lots of self-teaching, debugging, decision-making and collaboration that went on behind the scenes that made this project difficult, but much more rewarding when complete.
My journey in Dr. Tyrrell’s lab began when I first started researching ROP projects. I can still remember scrolling through the various projects, trying to find something that I thought I would be really passionate about. Once I happen upon Dr. Tyrrell’s ROP299, I could feel my heart skip a beat- it was exactly the research project that I was looking for. It explained the use of machine learning in medicine, specifically medical imaging. Being in bioinformatics, this project was exactly what I was looking for; it integrated biology and medicine with computer science and statistics. Once I saw this unique combination, I knew that I needed to apply.
After I applied, I was overjoyed that I had received an interview. When I attended the interview, I was very excited to show Dr. Tyrrell my interest in his research and explain how my past research would help me with this new project. But once I walked into his office, it was unlike any other interview I had ever had; he was able to point out things about myself that I had barely even realized and asked me many questions that I had no answer to. I remember walking out of that interview feeling disappointed as I thought that there was no way I would get a position in his lab, but a few weeks later heard back that I had gotten the position! I was delighted to have the opportunity to prove to Dr. Tyrrell that he made a good choice in choosing me for the position and that I would work hard in his lab and on my project.
The night before my first lab meeting, I researched tons of information on machine learning, making sure to have- what I thought- an in-depth understand of machine learning. But after less than five minutes into the lab meeting, I quickly realized that I was completely wrong. Terms like regression, weights, backpropagation were being thrown around so naturally, and I had absolutely no idea what they were talking about. I walked out of the meeting determined to really begin understanding what machine learning was all about!
Thus began my journey to begin my project. When I decided on my project, it seemed fun and not too difficult- all I have to do is slap on some heat maps to images, right? Well as much as I felt it wouldn’t be too difficult, I was not going to be deceived just as I had before attending our first meeting; and after completion I can definitely say it was not easy! The first problem that I encountered immediately was where to start. Sure, I understood the basic concepts associated with machine learning, but I had no experience or understanding of how to code anything related to creating and using a convolutional neural network. I was fortunate enough to be able to use Ariana’s CNN model. Her model used x-rays of teeth to classify if dental plates were damaged and therefore adding damage (artifacts) to the x-rays of teeth or if the plates were functional. It took me quite some time to understand what each line of code did within the program- the code was incredible, and I could not imagine having to write it from scratch! I then began the code to map the grad-CAM visualizations (resembling heat maps) onto the images that Ariana’s model took as input. I was again fortunate enough to find code online that was similar to what I needed for my project. I made very minor tweaks until the code was functional and worked how I needed it to. Throughout this process of trying to debug my own code or figure out why it wouldn’t even begin running, Mauro was always there to help, always being enthusiastic even when my problem was as silly as accidentally adding an extra period to a word.
Throughout the process, Dr. Tyrrell was always there as well- he always helped me to remember the big picture of what my project was about and what I was trying to accomplish during my time in his lab. This was extremely valuable, as it kept me from accidentally veering off-course and focusing on something that wasn’t important to my project. Without his guidance, I would have never been able to finish and execute the project in the way that I did and am proud of.
Everything that I learned, not only about machine learning, but about how to write a research paper, how to collaborate with others, how to learn from other’s and your own mistakes and how to keep trying new ideas and approaches when it seems like nothing is working, I will always carry with me throughout the rest of my undergraduate experience and the rest of my professional future. Thank you, Dr. Tyrrell, for this experience and every opportunity I was given in your lab.
Dianna McAllister