This survey provides a comprehensive overview of recent advances in out-of-distribution (OOD) detection in medical image analysis. The authors first explore several factors that can lead to distributional shifts in real-world clinical scenarios, and define three types of distributional shifts: contextual shift, semantic shift, and covariate shift.
A solution framework is then proposed to organize the existing research, categorizing the methods into five groups based on their underlying principles: post-hoc feature process, learning-free uncertainty quantification (UQ), learning-based deterministic UQ, OOD-aware training, and unsupervised stand-alone detectors. The association between OOD detection methods and the base task model is also discussed, highlighting the differences in deployment complexity.
The survey then systematically reviews the studies on OOD detection in two widely studied medical image analysis tasks: supervised medical image classification and medical image segmentation. For each method, the technical details and experimental settings are summarized. The evaluation protocols, metrics, and test samples corresponding to the three proposed OOD types are also provided.
Finally, the authors discuss a challenge in this area and identify a research direction that deserves more attention in future work.
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by Zesheng Hong... at arxiv.org 04-30-2024
https://arxiv.org/pdf/2404.18279.pdfDeeper Inquiries