Smailagic and team win Best Paper Award at IEEE Conference on Machine Learning and Applications

Artificially intelligent medical imaging technology is a promising tool to help doctors diagnose disease. But training these systems requires a lot of data, which is time-consuming and costly to obtain. However, Asim Smailagic, CEE's Hae Young Noh, and their team have recently been awarded the Best Paper Award at the IEEE Conference on Machine Learning and Applications for their work combatting this exact problem. In the paper, they introduce MedAL, an active learning approach that requires fewer training samples, maximizes the model’s performance, and overcomes the limitations of traditional approaches. Read more here.