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This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (NEURAL SPICING, Grant agreement No. 101002198), and the ERC starting Grant COMBIOSCOPY under Grant agreement No. ERC-2015-StG-37960. This project also received support from the Helmholtz Association under the joint research school "HIDSS4Health – Helmholtz Information and Data Science School for Health". Part of this work was funded by the Helmholtz Imaging Platform (HIP), a platform of the Helmholtz Incubator on Information and Data Science.
This is the python code implementation of the experiments presented in the manuscript titled "Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery". It's main components are the definitions of the deep learning model utilized for ischemia detection, the implementation of the region of interest tracking, and the code used to analyze inter-patient variability with mixed models.
surgery, video-rate, invertible neural networks, deep learning, real-time, spectral imaging, ischemia detection, out-of-distribution detection
surgery, video-rate, invertible neural networks, deep learning, real-time, spectral imaging, ischemia detection, out-of-distribution detection
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