# Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples
## 4 Discussion  
The main limitation of this study is the reduced number of patients and the imbalance of the dataset. Additionally, only 8 out of the 13 patients have annotated data available from the two classes, i.e., tumor and non-tumor. This fact, together with the known histological heterogeneity of GB tumors, makes this task very challenging. Therefore, more patients are needed in future analysis to obtain more robust conclusions about the ability of HSI and ML for the automatic detection of GB in histological slides. More concretely, to obtain statistically significant results, we hypothesize that at least 10 patients are needed with both types of annotations for both validation and test. This would require at least 40 patients with both types of annotations to train the classifiers. The main challenges for the acquisition of such a dataset are the large amounts of data generated during image acquisition and the time-consuming manual annotation for the ground truth. Further data acquisition campaigns will be deployed to increase our dataset, reinforcing the conclusions achieved in this work about the potential  
Figure 15: Heat and classification maps of four example non-tumor images. First row shows the synthetic RGB image of the HS cube, while second row represents the SLIC result. Third row represents the heat map, where red colors represent higher tumor probability. Fourth row shows the classification map, where red, green and white colors indicate tumor, non-tumor and light superpixels, respectively. In the last row, the tumor presence probability of the image is indicated. HS images with classification results with tumor probability \(>=50\%\) were considered tumor images, while results \(<\)50% were considered non-tumor images. use of the HS superpixel-based approach to detect GB in histological slides. Regarding the imbalanced dataset, in this manuscript we deal with this problem by using a hyperparameter optimization guided by metrics which may compensate the data imbalance, i.e., balanced accuracy and sensitivity. The use of sensitivity was motivated by the lower number of examples in the tumor class (\(\sim\)35% of tumor superpixels, compared to \(\sim\)65% of non-tumor superpixels). Nevertheless, other metrics can be used to drive the hyperparameter optimization to alleviate the effect of the imbalanced dataset. For example, the F-1 score or a weight balance between precision and recall, where the weight will favor the class with lower number of samples. Future studies will address some limitations of this work. First, in this work, the images were collected in a laboratory environment under controlled conditions. One of the main challenges associated to medical imaging processing is dealing with noisy images. Although in this work we focused in investigating the potential of HS images for the identification of tumor, in future works we will further analyze the effect of noise removal strategies in both the superpixel and the classification approaches [32; 33; 34]. Second, as previously mentioned, due to the macroscopic annotations, some regions of the tumor HS images are in fact not characteristic of GB tumor, but are still labeled as tumor. Such misannotated data are likely to produce superpixel misclassifications. To handle this fact, one of the future works in this field will be to identify which superpixel spectra can be found in both tumor and non-tumor images, which may improve the quality of the predictions. Third, although the spatial information of the HS is partially considered when the superpixels are extracted with SLIC, the supervised classification is only focused on the spectral information. In this study, we demonstrated the potential of the exclusive use of spectral information for the identification of tumor areas in HS digitized slides. Nevertheless, these results can be further improved if we also include additional spatial information into the classification framework. For example, as shown in the work by [PERSON] et al.