Using Synthesized Medical Images to Bridge the Gap between Medical Imaging Machines
If you’re old enough to remember ‘flip phones,’ then you might remember the first time phones had cameras. Fast forward 10-20 or so odd years – now, phone cameras have front and back lenses with incredible resolution and the latest image processing technology. Now, imagine taking a picture of a dog with a flip phone from the early 2000s and with another phone released in 2023. The dog remains the same, but the image itself vastly differs. This is what is known as domain shift in medical imaging technology; the equipment and user used to capture the same object differs. Specifically in medicine, hospitals use different brands and specifications of equipment acquired from various vendors, which can depend on their resources and budget.
Causal Inference and Social Networks: How to Quantify the Effects of our Peers
“If all of your friends jumped off a cliff, would you jump too?” While this comeback may be just an annoying retort to many teenagers, it presents an interesting question – what is the effect of social influence? This is what Ogburn, Sofrygin, Diaz, and van der Laan explore in their paper, “Causal Inference for Social Network Data”. More specifically, they are interested in developing methods to estimate causal effects in social networks and applying this to data from the Framingham Heart Study.