Computer Vision News Computer Vision News 12 In this paper, Carsten and Kim propose ValUES, a framework for systematically analyzing and evaluating uncertainty methods for image segmentation tasks. By defining the essential components of these theoretical methods and establishing benchmarks for comparison, ValUES empowers practitioners to make informed decisions about using them in their downstream tasks. Carsten Lüth and Kim-Celine Kahl are PhD students in the Interactive Machine Learning Group at the German Cancer Research Centre DKFZ under the supervision of Paul Jaeger. Their work on uncertainty estimation in semantic segmentation was selected for a coveted oral presentation at ICLR 2024 - no mean feat when only 1% of submissions made the cut - and they are here to tell us more about its innovative framework and implications for real-world medical applications. ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation ICLR Oral Presentation
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