Abstract
Hyper spectral imaging is a possible way for disease detection. However, for carcinoma detection most of the results are ex-vivo. However, in-vivo results of endoscopic studies still show fairly low accuracies in contrast to the good results of many ex-vivo studies. To overcome this problem and to provide a reasonable explanation, Monte-Carlo simulations of photon trajectories are proposed as a tool to generate multi spectral images including inter patient variations to simulate 40 patients. Furthermore, these simulations have the huge advantage that the position of the carcinoma is known. Due to this, the effect of mislabelled data can be studied. As shown in this study, a percentage of 30–35% of mislabelled data might lead to significant decrease of the accuracy from around 90% to around 70–75%. Therefore, the main focus of hyper spectral imaging has to be the exact characterization of the training data in the future.
Highlights
Hyper spectral imaging is a possible way for disease detection
It is expected that they may improve the methodology of virtual chromoendoscopy further
The high accuracy of virtual chromoendoscopy provides a hint that extracted fine spectral features allow a better accuracy than normal high definition white light endoscopy (HD-WLE)
Summary
Hyper spectral imaging is a possible way for disease detection. for carcinoma detection most of the results are ex-vivo. In-vivo results of endoscopic studies still show fairly low accuracies in contrast to the good results of many ex-vivo studies To overcome this problem and to provide a reasonable explanation, Monte-Carlo simulations of photon trajectories are proposed as a tool to generate multi spectral images including inter patient variations to simulate 40 patients. These simulations have the huge advantage that the position of the carcinoma is known. The in-vivo results[22] are outperformed by around 20 per cent points by the good ex-vivo classification results[20] To overcome this issue, this study tries to find the reason which might explain the limited quality of in-vivo multi/hyper spectral classification results with the focus on endoscopic problems. The in-vivo data set does not provide a controlled environment
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