Out of Distribution Detection for Intra-operative Functional Imaging
| Autor: | Ullrich Köthe, Leonardo Ayala, Anant Vemuri, Lynton Ardizzone, Carsten Rother, Beat P. Müller-Stich, Hannes Kenngott, Lena Maier-Hein, Tim Adler |
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| Rok vydání: | 2019 |
| Předmět: |
Intra operative
Artificial neural network business.industry Computer science Multispectral image Pattern recognition Spectral line 030218 nuclear medicine & medical imaging Functional imaging 03 medical and health sciences 0302 clinical medicine Optical imaging Distribution (mathematics) Key (cryptography) Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures-First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures ISBN: 9783030326883 |
| DOI: | 10.1007/978-3-030-32689-0_8 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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