Are We Ready for Out-of-Distribution Detection in Digital Pathology?

TL;DR

This paper evaluates the effectiveness of out-of-distribution detection methods in digital pathology, highlighting the need for proper evaluation protocols and exploring various models and training settings.

Contribution

It establishes a comprehensive benchmark for OOD detection in digital pathology, comparing multiple detectors, architectures, and training paradigms.

Findings

Proper evaluation protocols are crucial for OOD detection in DP.

Transformers and transfer learning impact OOD detection performance.

Benchmark results guide future research in digital pathology OOD detection.

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.