Title: Out of distribution detection for intra-operative functional imaging Show Chinese title

Title: 术中功能成像的分布外检测

Abstract: Multispectral optical imaging is becoming a key tool in the operating room. Recent research has shown that machine learning algorithms can be used to convert pixel-wise reflectance measurements to tissue parameters, such as oxygenation. However, the accuracy of these algorithms can only be guaranteed if the spectra acquired during surgery match the ones seen during training. It is therefore of great interest to detect so-called out of distribution (OoD) spectra to prevent the algorithm from presenting spurious results. In this paper we present an information theory based approach to OoD detection based on the widely applicable information criterion (WAIC). Our work builds upon recent methodology related to invertible neural networks (INN). Specifically, we make use of an ensemble of INNs as we need their tractable Jacobians in order to compute the WAIC. Comprehensive experiments with in silico, and in vivo multispectral imaging data indicate that our approach is well-suited for OoD detection. Our method could thus be an important step towards reliable functional imaging in the operating room. Show Chinese abstract

Abstract: 多光谱光学成像正成为手术室中的关键技术工具。最近的研究表明，机器学习算法可以用于将像素级的反射率测量值转换为组织参数，例如氧合度。然而，这些算法的准确性只能在手术过程中获取的光谱与训练期间看到的光谱匹配时才能得到保证。因此，检测所谓的分布外（OoD）光谱以防止算法呈现虚假结果具有重要意义。在本文中，我们提出了一种基于信息论的方法来检测OoD光谱，该方法基于广义可用信息准则（WAIC）。我们的工作借鉴了最近与可逆神经网络（INN）相关的技术。具体而言，我们需要使用一组可逆神经网络的可追踪雅可比矩阵来计算WAIC。通过体内和体外多光谱成像数据的综合实验表明，我们的方法非常适合用于OoD检测。因此，这种方法可能是手术室内可靠功能成像的重要一步。