In this episode, I talk with Mahyar Salek, co-founder and CTO of Deepcell, about an AI powered technology for single cell analysis through the lense of high content cell morphology. Deepcell's platform blends deep learning, microfluidics, and high resolution optics to deliver novel insights about cell biology and has the capability to sort, label-free for downstream multi-omic and functional analysis for use in research, translational studies, and therapeutic research. We discussed some of the challenges and opportunities in working with single cell images and how they used self-supervised learning.

“We really use the power of computer vision and AI capabilities combined with the advances in microfluidics and imaging to create this high dimensional, high content interpretation of single cell images. And we use that in real time to purify and separate cells of interest.”

“We have to see millions of cells even in just one go, one run. So you can't really do that without the scalability of an algorithm, right? And then we have to be consistent and robust.”

“When I hear challenges, I equate them with opportunities and I'll tell you why. So, for instance, one of the challenges, not just with us, but any sort of AI solution that looks at biological samples is the susceptibility to artifacts.”

“But as soon as you roll it out, there's a difference between your lab and the lab, you know, a block down the road because of the artifacts. So it's artifacts are definitely challenging, but for us, it's an opportunity as I mentioned, because we generate the data through our own platform and that means that we have a very controlled environment.”

“Because, again, we have the full control over the imaging path and where the cells lie, where we image them, we could actually do these sort of things and come up with models that are very less reliant on labels.”

“By being able to run a biological assay and validate whether the existing model, like basically errors in the existing models and existing labels, and that way you, you're able to iterate very quickly on your learning without even relying on an arguably erroneous human labels, erroneous and obviously expensive human labels.”

“Any modern life science companies that rely on data, you have to have a very tight collaboration between machine learning and data scientists and the domain experts.”

“It is really important to, as you kind of come up with a development strategy and the product strategy, understand where you could rely on AI today versus where you hope that the AI could deliver, you know, two years down the road.”

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.
Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.
Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.
Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.