Are We Ready for Out-of-Distribution Detection in Digital Pathology?
This addresses a critical but overlooked problem for digital pathology applications, though it is incremental as it benchmarks existing methods.
The paper tackled the challenge of out-of-distribution detection in digital pathology by establishing a benchmark study, comparing diverse detectors and exploring advanced ML settings, resulting in new insights and guidelines for future research.
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.