bibtext: @InProceedings{ Oh_Are_MICCAI2024,
                   author = { Oh, Ji-Hun and Falahkheirkhah, Kianoush and Bhargava, Rohit },
                   title = { { Are We Ready for Out-of-Distribution Detection in Digital Pathology? } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15010 },
                   month = {October},
                   pages = { pending },
                }
abstract: The detection of semantic and covariate out-of-distribution (OOD) examples is a critical yet overlooked challenge in digital pathology (DP). Recently, substantial insight and methods on OOD detection were presented by the ML community, but how do they fare in DP applications? To this end, we establish a benchmark study, our highlights being: 1) the adoption of proper evaluation protocols, 2) the comparison of diverse detectors in both a single and multi-model setting, and 3) the exploration into advanced ML settings like transfer learning (ImageNet vs. DP pre-training) and choice of architecture (CNNs vs. transformers). Through our comprehensive experiments, we contribute new insights and guidelines, paving the way for future research and discussion.
title: Are We Ready for Out-of-Distribution Detection in Digital Pathology?
authors: Oh, Ji-Hun Falahkheirkhah, Kianoush Bhargava, Rohit