Out of distribution detection for intra-operative functional imaging
This addresses the need for reliable functional imaging in operating rooms by improving safety and accuracy, though it is incremental as it builds on existing invertible neural network methodology.
The paper tackles the problem of detecting out-of-distribution spectra in multispectral optical imaging during surgery to prevent inaccurate tissue parameter predictions, presenting an information theory-based approach using invertible neural networks and WAIC that shows suitability in experiments with in silico and in vivo data.
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.