# Deep Neural Networks for HER2 Grading of Whole Slide Images with Subclasses Levels
The inter and intra variability in human diagnosis, as well as the heterogeneous colour intensities of the stain applied, makes this problem suitable for an end-to-end deep learning methodology, which can learn features independent from human perception [6,7]. Moreover, the increasing adoption of digital pathology and higher availability of a large number of well-annotated WSIs allows taking advantage of deep learning techniques to train more accurate and robust models [8]. This kind of methods has been proved to increase the pathologists precision and speed in diagnosis, in comparison with non-aided approaches [9,10]. This idea is further supported by studies showing that glass slides and digital images are equally accurate for HER2 interpretation in pathology prognosis [11,12]. HER2 scoring typically involves the classification of small patches extracted from the WSI. These patches are then used to generate predictions at the patch level, which are then aggregated to obtain a prediction for the entire slide. There are two approaches to patch classification: fully supervised and weakly supervised. In fully supervised approaches, the task involves classifying every patch within a segmented region. Weakly supervised methods, based on pathologists' slide evaluation practices, use models to learn patch selection. According to some authors, the latest methods are limited by the need for patch-level labelling, which is not typically used in clinical evaluation. Other studies find that segmenting infiltrating cancer patches does not affect HER2 scoring, despite potential improvements [5]. In the following we provide a comprehensive review of the research works published from 2018 to 2023, focusing on various techniques used to assess the scoring of Her2. [PERSON] _et al._[13] present a deep learning framework (HER2 Net) for cell segmentation and patch scoring in HER2-stained breast cancer images. HER2 Net uses trapezoidal long short-term memory, spatial pyramid pooling, and convolutional and deconvolutional layers. The framework yields a general precision of 96.64%, an accuracy of 98.33% and an F-score of 96.71% in classifying the cell membrane staining pattern as no staining, faint staining, \begin{table}
\begin{tabular}{c l l} \hline \hline
**Score** & **Staining Patterns** & **HER2 Assessment** \\ \hline
0 & \textless{}10\% of cells with weak staining & Negative \\ \hline
1 & \textless{}10\% of cells with moderate staining & Negative \\  & and more than 10\% of cells with weak staining & \\ \hline
2+ & \textless{}10\% of cells with strong staining & Equivocal \\  & and \(\geq\)10\% of cells with moderate staining & \\ \hline
3+ & \(\geq\)10\% of cells with strong staining & Positive \\ \hline \hline \end{tabular}
\end{table} 
Table 1: HER2 diagnosis rule followed by pathologists. Figure 1: HER2 images samples and their classification: (a) class 0; (b) class 1; (c) class 2+; (d) class 3+; and (e) Background. moderate staining, and strong complete staining. Results were obtained from 752 patches cropped from 79 WSIs. This method published in 2018 [13] achieves the highest accuracy value reported thus far. However, it does not differentiate between the segmentation and classification results. The results are presented by modifying the number of training and test patch images, sometimes even using zero training images, which renders the results unclear. Ultimately, a comprehensive score of the entire WSI is missing. [PERSON] _et al._[14] introduce an automated scoring HER-2 algorithm that relies on colour hand-crafted features and traditional machine learning techniques. This is the initial method for HER2 scoring that does not involve segmentation prior to the classification process. As stated in the literature review, the majority of methods involve segmentation, which is recognised for its tendency to introduce errors in subsequent stages. They claim that their method achieved a 94.12% accuracy rate, without the need for explicit segmentation and extraction of structural properties such as cell nuclei and membrane.