Purpose: A fundamental problem in designing safe machine learning systems is identifying when samples presented to a deployed model differ from those observed at training time. Detecting so-called out-of-distribution (OoD) samples is crucial in safety-critical applications such as robotically guided retinal microsurgery, where distances between the instrument and the retina are derived from sequences of 1D images that are acquired by an instrument-integrated optical coherence tomography (iiOCT) probe. Methods: This work investigates the feasibility of using an OoD detector to identify when images from the iiOCT probe are inappropriate for subsequent machine learning-based distance estimation. We show how a simple OoD detector based on the Mahalanobis distance can successfully reject corrupted samples coming from real-world ex vivo porcine eyes. Results: Our results demonstrate that the proposed approach can successfully detect OoD samples and help maintain the performance of the downstream task within reasonable levels. MahaAD outperformed a supervised approach trained on the same kind of corruptions and achieved the best performance in detecting OoD cases from a collection of iiOCT samples with real-world corruptions. Conclusion: The results indicate that detecting corrupted iiOCT data through OoD detection is feasible and does not need prior knowledge of possible corruptions. Consequently, MahaAD could aid in ensuring patient safety during robotically guided microsurgery by preventing deployed prediction models from estimating distances that put the patient at risk.

翻译：目的：设计安全机器学习系统的一个基本问题是，识别部署模型所接收的样本是否与训练时观察到的样本存在差异。在机器人辅助视网膜显微手术等安全关键应用中，检测所谓的分布外（OoD）样本至关重要，因为器械与视网膜之间的距离是通过器械集成光学相干断层扫描（iiOCT）探头获取的一维图像序列来推算的。方法：本研究探讨了利用OoD检测器识别iiOCT探头图像是否不适用于后续基于机器学习的距离估计的可行性。我们展示了基于马氏距离的简单OoD检测器如何成功拒绝来自真实世界离体猪眼的损坏样本。结果：我们的结果表明，所提方法能够成功检测OoD样本，并将下游任务的性能维持在合理水平。MahaAD优于基于同类损坏训练的有监督方法，在检测包含真实世界损坏的iiOCT样本集中OoD案例时取得了最佳性能。结论：结果证实，通过OoD检测识别损坏的iiOCT数据是可行的，且无需预先了解可能的损坏类型。因此，MahaAD可通过防止部署的预测模型估算出危及患者安全的距离，从而有助于在机器人辅助显微手术中确保患者安全。