# Pathomics and Deep Learning Classification of a Heterogeneous Fluorescence Histology Image Dataset
## 4 Discussion  
This study was mainly focused on the classification of a publically available fluorescence dataset. The dataset contained 41 images of normal nuclei of human cells and 38 images of pathological nuclei from three different types of rare pediatric embryonal tumors. Two of them, neuroblastoma and ganglloneuroblastoma, are neuroblastic tumors of different grades of differentiation and malignancy which belong to the group of tumors arising from the sympathoadrenal lineage of the neural crest during development, while the third one, [PERSON] tumor (nephroblastoma) is a malignant embryonal neoplasm derived from nephrogenic blastemal cells. Two different AI pipelines based on pathomics and deep learning were implemented for the automated classification between normal benign and malignant types of nuclei as labelled by the expert. The classification was a challenging \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Feature Type**} & \multirow{3}{*}{
\begin{tabular}{c} **Model** \\ **Family** \\ \end{tabular} } & \multicolumn{3}{c}{**SVM RBF**} & \multicolumn{3}{c}{**Logistic Regression OVR**} \\ \cline{3-8}  & & **Variance** & & & **Variance** & & \\  & & **Threshold** & **ACC** & **AUC** & **Threshold** & **ACC** & **AUC** \\ \hline \multirow{8}{*}{Raw} & Xception & 0.0 & _0.943 \(\pm\) 0.07_ & _0.956 \(\pm\) 0.05_ & 0.0 & 0.925 \(\pm\) 0.08 & 0.944 \(\pm\) 0.07 \\  & VGG & 0.3 & 0.646 \(\pm\) 0.09 & 0.664 \(\pm\) 0.08 & 0.5 & 0.905 \(\pm\) 0.07 & 0.925 \(\pm\) 0.06 \\  & ResNet & 0.4 & 0.876 \(\pm\) 0.07 & 0.898 \(\pm\) 0.07 & 0.3 & 0.924 \(\pm\) 0.10 & 0.943 \(\pm\) 0.07 \\  & Inception & 0.5 & 0.916 \(\pm\) 0.09 & 0.929 \(\pm\) 0.07 & 0.5 & 0.942 \(\pm\) 0.06 & 0.960 \(\pm\) 0.04 \\  & MobileNet & 0.0 & 0.905 \(\pm\) 0.09 & 0.909 \(\pm\) 0.07 & 0.0 & 0.924 \(\pm\) 0.09 & 0.941 \(\pm\) 0.07 \\  & DenseNet & 0.1 & 0.926 \(\pm\) 0.13 & 0.940 \(\pm\) 0.10 & 0.4 & _0.944 \(\pm\) 0.06_ & _0.951_ \(\pm\) 0.05 \\  & NasNet & 0.0 & 0.897 \(\pm\) 0.10 & 0.908 \(\pm\) 0.09 & 0.0 & 0.907 \(\pm\) 0.07 & 0.926 \(\pm\) 0.05 \\  & Xception & 0.4 & _0.933 \(\pm\) 0.06_ & _0.943 \(\pm\) 0.05_ & 0.2 & 0.923 \(\pm\) 0.07 & 0.944 \(\pm\) 0.05 \\  & VGG & 0.2 & 0.659 \(\pm\) 0.06 & 0.679 \(\pm\) 0.04 & 0.1 & 0.905 \(\pm\) 0.06 & 0.922 \(\pm\) 0.04 \\  & ResNet & 0.1 & 0.876 \(\pm\) 0.10 & 0.892 \(\pm\) 0.10 & 0.2 & 0.926 \(\pm\) 0.11 & 0.940 \(\pm\) 0.10 \\  & Inception & 0.3 & 0.918 \(\pm\) 0.10 & 0.932 \(\pm\) 0.10 & 0.3 & 0.945 \(\pm\) 0.07 & 0.922 \(\pm\) 0.04 \\  & MobileNet & 0.3 & 0.907 \(\pm\) 0.08 & 0.921 \(\pm\) 0.07 & 0.4 & 0.914 \(\pm\) 0.07 & 0.934 \(\pm\) 0.06 \\  & DenseNet & 0.5 & 0.916 \(\pm\) 0.08 & 0.933 \(\pm\) 0.06 & 0.3 & **0.951 \(\pm\) 0.05** & **0.962 \(\pm\) 0.04** \\  & NasNet & 0.0 & 0.897 \(\pm\) 0.08 & 0.909 \(\pm\) 0.07 & 0.2 & 0.913 \(\pm\) 0.08 & 0.926 \(\pm\) 0.07 \\  & Xception & 0.2 & 0.944 \(\pm\) 0.06 & 0.953 \(\pm\) 0.06 & 0.2 & 0.925 \(\pm\) 0.08 & 0.938 \(\pm\) 0.07 \\  & VGG & 0.0 & 0.763 \(\pm\) 0.14 & 0.783 \(\pm\) 0.13 & 0.2 & 0.905 \(\pm\) 0.09 & 0.910 \(\pm\) 0.08 \\  & ResNet & 0.1 & 0.875 \(\pm\) 0.07 & 0.896 \(\pm\) 0.07 & 0.3 & _0.942 \(\pm\) 0.07_ & _0.954 \(\pm\) 0.06_ \\  & Inception & 0.3 & **0.945 \(\pm\) 0.06** & **0.959 \(\pm\) 0.05** & 0.0 & 0.935 \(\pm\) 0.07 & 0.951 \(\pm\) 0.06 \\  & MobileNet & 0.4 & 0.913 \(\pm\) 0.08 & 0.923 \(\pm\) 0.07 & 0.2 & 0.924 \(\pm\) 0.07 & 0.948 \(\pm\) 0.06 \\  & DenseNet & 0.2 & 0.935 \(\pm\) 0.06 & 0.941 \(\pm\) 0.06 & 0.4 & 0.935 \(\pm\) 0.07 & 0.948 \(\pm\) 0.06 \\  & NasNet & 0.2 & 0.915 \(\pm\) 0.09 & 0.919 \(\pm\) 0.09 & 0.0 & 0.925 \(\pm\) 0.07 & 0.942 \(\pm\) 0.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of nuclei image characterization is organized by feature type, architecture family, variance threshold and classifier. The best model performance for each feature type was highlighted with italics and the best overall model with bold.