# Deep Neural Networks for HER2 Grading of Whole Slide Images with Subclasses Levels
In addition, it is completely automated and can effortlessly operate on basic desktop computers. [PERSON] _et al._[15] propose a model that involves three main components: (1) classification of the whole slide image (WSI) into stroma and epithelium areas using support vector machine (SVM) classifier; (2) training the model using patches from the epithelial part for segmentation of cell membrane staining patterns; and (3) a scoring mechanism that combines tile results, transferring staining intensities and completeness to align with pathologists' assessments. Experimental results on 127 slides show an average accuracy of 95% for the segmentation but it shows lower performance on the classification stage with an average accuracy of 87%. This study provides a review of 9 previous methods, with accuracy ranging from 79% to 98%. The utmost accuracy is obtained with [PERSON] _et al._'s work, as mentioned earlier [13]. There are only two works that use WSIs. One of them has 86 WSIs [16], while the other one, which is their own work [15], has 127 WSIs. The remaining works uses patches derived from multiple WSIs. The number of patches extracted varies, ranging from 77 patches obtained from 77 WSIs, to 752 patches obtained from 79 WSIs, 1265 patches obtained from 253 WSIs, and 2580 patches obtained from 86 WSIs. In [17] [PERSON] _et al._ use colour deconvolution to separate channels, followed by segmentation of cell nuclei and boundaries. Then, hand-crafted cell-based features, reflecting membrane staining intensity and completeness, are extracted. Then, these features are fed into a classical machine learning classifier to determine HER2 scores. The final HER2 tissue score is derived by aggregating the individual cell scores, in accordance with ASCO/CAP guidelines. They also present a review of the same methods as in the previous study [15], but distinguishing between methods that rely on hand-crafted feature extraction and those that use deep learning. They argue that deep learning methods suffer from overfitting to the data, necessitating re-training or re-designing the model when new data is introduced. They claim that the hand-crafted features and classical machine learning approach they propose offer an explainable AI solution without requiring the model to be re-trained. Their average accuracy is 91.43% and their F1-score is 91.81%. In [18] [PERSON] _et al._ propose a hierarchical Pathologist-Tree Network (PTree-Net) with multi-instance learning (MIL) to use multi-scale features from the WSI pyramids. The method starts by training the Focal-Aware Module with slide labels to accurately detect and classify diagnostic regions. A Patch Relevance-enhanced Graph Convolutional Network then thoroughly analyses the hierarchical arrangement of multi-scale patches from attentive regions. Finally, tree-based self-supervision fine-tunes PTree-Net to improve representation learning and reduce unimportant patches. This method achieves average values of 89.70% for accuracy, 90.77% of F1-score and 83.34% for MCC with standard deviation of around 1.4 based on 4-fold cross validation on a dataset of 105 WSIs. The model leverages comprehensive information from the entire WSI without requiring hand-crafted features or rules. They combine classes 0 and 1+, and the confusion matrix from PTree-Net's 4-fold cross-validation shows that it performs well identifying WSIs with a 3+ HER2 score, achieving a classification rate of 95.24%, but performs poorly with a 2+ score,achieving 89.16%. The distinction between 2+ and 0/1+ categories presents difficulties, with 11.99% of 0/1+ samples misclassified as 2+ and 9.38% vice versa. The approach presented by [PERSON] _et al._[5] combines both fully and weakly supervised HER2 scoring paradigms, providing interpretability without the need for extensive expert annotations. Training and evaluation only use patches that cover more than 10% of the surface area affected by invasive carcinoma. This addresses 96% of invasive carcinoma's surface area in the user-specified ROI.