Viewing the world through a straw
How lessons from computer vision applications in geo will impact bio image analysis (Part 1)
By Nick Weir (Senior Data Scientist, In-Q-Tel CosmiQ Works), JJ BenJoseph (Member of Technical Staff, In-Q-Tel B.Next), and Dylan George (VP, Technical Staff, In-Q-Tel B.Next). This is part 1 of a collaboration between CosmiQ Works and B.Next, and is cross-posted at both blogs.
Marc Andreesen described software companies as “eating traditional business”. Similarly, computer vision has begun to eat the world of manual image analysis. However, when we step beyond standard photographs and enter niche domains, like biological imaging (medical imaging, microscopy data) and satellite imagery, this has proven less true. Computer vision is eating these data like a Charleston Chew that got left outside in a New England winter: struggling to bite into it, breaking off small pieces, then chewing ponderously before finally swallowing (or spitting it back out in frustration). IQT CosmiQ Works has closely tracked the maturation of AI and computer vision applications to satellite imagery, learning key lessons about the difficulties inherent to transitioning AI tools between domains. At the same time, CosmiQ Works and the IQT B.Next team have noticed that AI product development for medicine has lagged a few years behind related geospatial applications, and is only just beginning to hit its stride. In this blog series we will explore why AI has struggled to gain traction with both satellite imagery and medicine. We’ll also dig into some of the similarities and differences between satellite imagery, microscopy, and “normal” photographs, and why researchers developing AI methods for microscopy might want to scrutinize the work done in geospatial.
What is computer vision and how is it eating the world of imagery?
Computer vision is a term encompassing the wide variety of methods that computers use to interpret images and videos. Before ~2010, this was primarily achieved through a toolbox including edge detection, watershed segmentation, and other heuristic methods.