# Pathomics and Deep Learning Classification of a Heterogeneous Fluorescence Histology Image Dataset
## 3 Results
### Transfer Learning  
The experiments were performed on computational infrastructure featuring a 10-core Xeon processor with 32 gigabytes of RAM and an Nvidia GTX 1070 graphics card with 8 gigabytes of VRAM. The extraction of deep features from a single image requires approximately 14 ms to 426 ms depending on the architecture. Seven deep architecture families with a total of eighteen model variations were examined. The models were trained on ImageNet dataset, the neural network layers were rejected and three methods for extracting the deep features were applied, as described in Section 2.4.2. The \"off-the-shelf\" feature extractor transfer learning technique includes: (a) preserving convolutional layer weights from the Keras pretrained model, (b) introducing a new input of images of size 250  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Logistic Regression (OVR)**} & \multicolumn{2}{c}{**SVM RBF**} \\ \hline
**Selected** & **AUC** & **ACC** & **AUC** & **ACC** \\
**Features** & & & & \\ \hline
3 & 0.956 \(\pm\) 0.047 & 0.8 \(\pm\) 0.093 & 0.965 \(\pm\) 0.056 & 0.786 \(\pm\) 0.064 \\
6 & 0.968 \(\pm\) 0.033 & 0.871 \(\pm\) 0.103 & 0.954 \(\pm\) 0.042 & 0.843 \(\pm\) 0.118 \\
10 & 0.983 \(\pm\) 0.03 & 0.871 \(\pm\) 0.128 & 0.981 \(\pm\) 0.032 & 0.843 \(\pm\) 0.159 \\
**20** & **0.986 \(\pm\) 0.033** & **0.957 \(\pm\) 0.105** & **0.965 \(\pm\) 0.086** & **0.929 \(\pm\) 0.103** \\
30 & 0.992 \(\pm\) 0.019 & 0.886 \(\pm\) 0.064 & 0.978 \(\pm\) 0.033 & 0.914 \(\pm\) 0.035 \\
40 & 0.996 \(\pm\) 0.006 & 0.943 \(\pm\) 0.049 & 0.976 \(\pm\) 0.031 & 0.871 \(\pm\) 0.07 \\
50 & 0.99 \(\pm\) 0.019 & 0.943 \(\pm\) 0.073 & 0.986 \(\pm\) 0.033 & 0.886 \(\pm\) 0.064 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Area under the curve score and accuracy (mean \(\pm\) standard deviation) of classification per number of selected pathomics features for the two classifiers used. The best performance was highlighted with bold.  
by 250 pixels, and (c) removing fully connected and Softmax layers (Figure 2). To prevent samples from the same image being used in both the training and testing sets, a stratified 10-fold cross-validation technique was used on a unique image identifier basis.  
Additionally, five variance thresholds were integrated ranging from 0.0 to 0.5 significantly reducing the dimensionality of deep descriptors. Finally, traditional machine learning classifiers were trained with a new per deep descriptor labeling for distinguishing among normal, benign, and malignant nuclei, and they were capable of reaching a testing ACC of up to 0.945 \(\pm\) 0.06. In terms of ACC performance, the Xception (0.923-0.944), Inception (0.916-0.945), and DenseNet (0.916-0.951) were the top performing deep descriptors across all three feature types, as can be observed in Table 2. It is worth noting that models based on the VGG and ResNet families consistently gave an error higher or equivalent (12.3-35.4%) than the benign class distribution despite the higher score of separability (AUC up to 0.896), indicating that these models have likely been biased toward the minority class.