## 3 Experimental Setup
### Materials
#### 3.1.2 Synthetic Images  
As previously mentioned, the main goal of this work was to evaluate the effectiveness of the direct image classification approach proposed by other authors, which in our opinion was too sensitive to the coarse-grained details, such as the number and distribution of \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **Work** & **Task** & **Approach** \\ \hline \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & [20] & WBC detection & detection from entire images \\  & [36] & WBC detection & two-steps approach \\  & [39] & WBC detection + classification & detection from entire images \\  & [38] & WBC segmentation + classification & work on single WBC \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & [40] & WBC types classification & single WBC approach \\  & [41] & WBC types classification & single WBC approach \\  & [24] & Blood cells classification & work on single cells \\  & [19] & WBC types classification & single WBC analysis \\  & [37] & ALL classification & work on single WBC \\  & [42] & ALL classification & **entire image** \\  & [21] & ALL classification & **entire image** \\  & [23] & ALL classification & **entire image** with fine-tuning \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of existing works in this field with key insights from the proposed methods. leukocytes, rather than to fine-grained details such as cell textures and shapes. To this aim, for each original ALL-IDB image belonging to the healthy class, we created different synthetic versions, adding other healthy leukocytes belonging to ALL-IDB2. Several approaches have been proposed for creating synthetic images, and most of them are based on DNN and, in particular, on Generative Adversarial Networks (GANs) [46; 47]. Despite the great success of GANs, they do not have an encoder, which makes them difficult to train, and at the same time, it is not easy to force their equivalence to the desired factors. This means that one cannot have complete control over the type and features of images they produce as output. Instead, we aimed to produce images with specific features to be used as an additional testing set. For this reason, we used a relatively simple approach for image synthesis based on cut and paste, also called a \"smart augmentation\" process [46]. Firstly, we built a GUI that allowed us to select the locations where the synthetic leukocytes could be placed inside the original ALL-IDB1 images. In order to create the synthetic images without visually altering the original cells, we manually selected the plasma regions (where no cells were present) of appropriate sizes as candidate positions for placing the cells. Thus, the final number of cells artificially added to the original image was strictly related to the nature of the image itself; the higher the number of appropriate plasma regions, the higher the number of added cells. In addition, a bounding box (BB) was drawn for each candidate position (see Figure 3a) in order to define the size of the cell that it could accommodate. Then, given that our aim was to create synthetic images with leukocytes but without leukaemia, we selected and computed the size of all the healthy cells in ALL-IDB2, from now on called candidate WBC.