Imagine trying to guess whether a spot on a slide is cancerous or not. It's a tough job, especially when you have a lot of slides but no answers to check your guesses against. That's the challenge in digital pathology: lots of unlabeled slides, not enough clear answers. Now, think of a smart comput
Imagine you're a doctor trying to understand how someone's nose works, or you're planning a delicate surgery. You'd need to see a super clear image of the nasal cavity and the tiny details inside. Head CT scans can help, but spotting the differences between all the tiny structures in the nose isn't
Have you ever wondered when a document on the web was created? It’s not always easy to tell, and that’s a problem. Sometimes, that little timestamp at the bottom is missing or can’t be trusted. So, scientists have come up with a clever idea: using the content of the document to figure it out. Meet
In 2008, a fresh computer science grad student at Princeton sat in on a lecture about neural networks. The topic seemed outdated, like something from the '80s and '90s that had lost its spark. Researchers were more interested in sleek algorithms like support vector machines. Unbeknownst to this stud
Imagine trying to understand how you walk with just your smartphone. Researchers did exactly that! They created a clever algorithm using neural networks to detect when your foot hits the ground (heel strike) and when it lifts off (toe-off) while carrying a phone. Fifty-two adults walked on a treadmi
