Computer Vision News - April 2023

50 Challenge at ISBI see with MRI, where there’s just a lot of data. We provide in vivo data for the second track, but it comes from the same vendor and has similar parameters. Then on the third track, we provide data from three different vendors with different parameters . The idea is that each track of the challenge is a little bit harder than the previous one. ” The challenge is open to everybody and could be a great entry point for machine learning engineers and data scientists looking to explore medical imaging or people already familiar with MRS but wanting to enter the machine learning field. The team has a fascinating website with tutorials and guides to help you get started, and they are happy to answer any questions. The challenge is Rodrigo and Hanna’s first, but they have their supervisors – Ashley Harris , who works at the Alberta Children’s Hospital , and Roberto Souza , who co-conceived the Calgary-Campinas If a patient was tracked, could that diminish the impact of their movement? “ Prospective motion correction is still in the works, ” Hanna responds. “ As far as I know, it doesn’t exist for edited MRS. Retrospective motion correction algorithms do exist. They’re not as good for MRI as for MRS, but even for MRS, they’re not great. ” Competitors are asked to develop ways to reconstruct different spectra from data with fewer transients than regular scans. Usually, there would be 320 transients per scan, but they are given 80. The challenge has three tracks; people can participate in one, two, or all three. The goal is to go from a specific to a more generalized model. “ The first track is with simulated data because there isn’t enough MRS data, ” Rodrigo points out. “ A few researchers are working on it, but it’s not enough to train machine learning models as we