# Pathomics and Deep Learning Classification of a Heterogeneous Fluorescence Histology Image Dataset
## 4 Discussion
task for the AI pipelines since the dataset exhibited significant heterogeneity in terms of vendor, image size, magnification, and signal-to-noise ratio. The first step of our analysis was to address the heterogeneity in nuclei sizes emanating from different magnifications and image sizes since size and shape related features from the pyradiomics library could potentially introduce exaggerated values not corresponding to actual differences and lead to unreliable results. To overcome this limitation, an adaptive pre-processing technique in Section 2.2 was proposed to ensure that in all images, nuclei sizes fall within a similar size range. This harmonization step was used for both presented ML analyses in order to ensure uniform nuclei image dimensions. Regarding the classification performance with pathomics feature extraction, we experimentally showed by repeating the classification with a different number of selected features using the pymrmr algorithm, that for 20 selected features the minimum error was achieved, as presented in Table 1 for both classifiers. Additionally, it is noteworthy that when using more than 20 selected features, the performance accuracy drops (Table 1). Despite the heterogeneous nature of the dataset, classification through pathomics analysis exhibited the highest performance with an AUC of 0.986 and an ACC of 0.957 regarding the logistic regression classifier. In a similar way, the SVM RBF classifier performed almost equally to the logistic regression, with slight differences presenting an AUC of 0.965 and an ACC of 0.929. An additional harmonization pre-processing stage involving image cropping and padding in the DL approach was necessary prior to performing the feature extraction from the pretrained models (Section 2.4.1) to ensure consistent input image size. Due to the small size of the examined dataset, only transfer learning techniques were considered. The DenseNet consistently achieved the highest performance (up to ACC 0.951 \(\pm\) 0.05 and AUC 0.962 \(\pm\) 0.04) regardless of the employed pooling technique, as shown in Table 2. The state-of-the-art performance of the proposed methodology demonstrates the feasibility of DL analysis in fluorescence histology image analysis and modelling despite the limited size of the available data. This encouraging result indicates that AI can be used with advantage to address clinical unmet needs in fluorescence pathology image analysis. To this end, creating larger, labelled and diverse datasets in terms of vendor and image settings is of utmost importance for developing more generalizable and trustworthy AI models in this field. The pathomics-based models with 3, 6, 10 (LR and SVM RBF) and 40, 50 (SVM RBF) features, as well as the deep descriptors from VGG, ResNet (SVM RBF with all the three types of deep features) in Table 2, may have made biased predictions because the prediction error (12.3-20%, Tables 1 and 2) is higher or equal to the minority class distribution (12.7%). Regardless of the fact that the classifier seems to be capable of effectively separating samples from the three classes suggested by the high AUC value, the lower accuracy score (ACC) in this case indicates a biased classifier. Leveraging AI for characterizing vast amounts of pathology image data can spare clinical experts from tedious and time-consuming tasks, thus alleviating their heavy workload. At the same time, the collaboration of humans and AI has the potential to augment the overall efficiency of the decision-making process based on pathology image analysis. We are aware that our research has some limitations. The first limitation arises from the relatively small size of the dataset used (\(N=79\) images). That said, the analysis pipeline was carefully selected considering the size of the dataset size, and this was the main reason that more traditional techniques were used. The proposed pipeline should be further evaluated in larger and even more diverse datasets to promote the generalizability of the results.