Computer Vision News 30 This research has established a new task in the domain of machine learning. Many studies focus on well-established tasks with good baselines and data, whereas this work tackles an essential yet unexplored task due to a lack of prior baselines. Also, despite its importance in predicting outcomes, the tedious nature of AgNOR assessment has thus far prevented its integration into routine pathologist workflows. What do Jonathan and Marc think convinced the judges to award the paper the top prize at BVM this year? “Oh, that’s a tough question,” Jonathan remarks. “We did science. We didn’t over-advertise what we did. We know what our algorithm is able to do, and we highlighted our limitations.” Marc adds: “It was just a rock-solid science paper. It was targeted toward insights, not methods. We wanted to find out stuff, see if that’s possible, and how well we do against a human baseline. It was really just the science focus of the paper that the reviewers agreed was actually good. They were happy it wasn’t yet another new method that does yet another 1% increase in something, which is, I think, what many people are a little bit tired of!” Check out our video interview to learn more about this work, including Jonathan andMarc’s ideas for extending it, their ambitions to work on different focal planes, and their regular work around tumor biology. Best Paper Award at BVM Check out our Video Interview with Marc and Jonathan
RkJQdWJsaXNoZXIy NTc3NzU=