Figure 5 | Scientific Reports

Figure 5

From: Spectral organ fingerprints for machine learning-based intraoperative tissue classification with hyperspectral imaging in a porcine model

Figure 5

Results of deep learning-based organ classification. (a) confusion matrix which was generated for a hold-out test set comprising 9895 annotations from 5293 images of 8 pigs that were not part of the training data. Confusion matrices were calculated and column-wise normalized (i.e. divided by the column sum) per pig based on the absolute number of (mis-)classified annotations. These normalized confusion matrices were averaged across pigs while ignoring non-existent entries (e.g. due to missing organs for one pig). Each value in the matrix thus depicts the average fraction of annotations which were labeled as the column class and predicted as the row class. Numbers in brackets depict the standard deviation across pigs. Zero values are not shown in the confusion matrix in order to improve visibility. Since multiple organs can appear on the same image, the number of annotations exceeds the number of images. (b) Exemplary image with multiple organ annotations by an expert. (c) Organs classified through deep learning.

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