# Deep Neural Networks for HER2 Grading of Whole Slide Images with Subclasses Levels
The model attains an F1-score of 78% on the hold-out test set, along with an average precision of 85.4% with very small standard deviation of 0.05. Additionally, the model achieves a Dice score of 0.91 for class 2+ and 3+ slides, while exhibiting lower scores for classes 0 and 1+. They claim that the use of ASCO/CAP clinical guidelines directly in training and testing contributes to the model's interpretability and alignment with clinical standards. The work of [PERSON] _et al._[19] is the first to study the uncertainty in the classification of Her2 histopathological images into the categories of 0, 1+, 2+, and 3+ at the patch level. The process consists of four stages: WSI pre-processing for colour deconvolution, generation of patch dataset, classification based on Bayesian deep learning, and prediction with estimation of uncertainty. The proposed approach integrates deep learning and Monte Carlo Dropout to quantify uncertainty, a factor frequently overlooked in healthcare applications. This method achieves an average accuracy of 89%, precision of 81%, and recall of 74% for tissue-level classification on the dataset of the Her2 challenge contest [16] with 172 WSIs. In [7] the authors present a novel study by integrating FISH images alongside IHC images during the training phase. The study presents a machine learning model for HER2 classification, based on logistic regression. The model was trained using 393 IHC images to distinguish between upregulated and normal HER2 expression. Pathologists' diagnoses (IHC only) and final diagnoses (IHC + FISH) were used for training. The IHC model achieved an accuracy of 88%, precision of 89%, and recall of 43%. In comparison, the IHC + FISH classifier achieved an accuracy of 93%, precision of 100%, and recall of 55%. The model exhibited superior performance when trained using IHC + FISH diagnoses, highlighting the significance of subcellular staining patterns in pathological diagnosis prior to FISH consultation, as opposed to overall intensity. Finally, the work of [PERSON] _et al._[20] use deep learning to automatically assess breast cancer Her2 WSI scores on 95 IHC section images with labelled tumour areas. The proposed method has three steps. After extracting labelled masks from WSIs, tumour and normal patches were randomly generated to create a probability map. Applying a threshold to the probability map yielded the binary tumour prediction. Finally, a deep learning model (ResNet34) was fed the patches to improve binary classification. WSI test classes were 0, 1+, 2+, and 3+. The results show 73.49% segmentation accuracy and 95.77% precision at the patch level. On the test set, the F1 score is 83.09% and the accuracy is 97.9%. The study acknowledges limitations. First, dye dosage during slicing can cause colour depth inconsistencies. Colour standardisation in deep learning methods improves tumour area identification but increases execution time without substantially affecting IHC score accuracy. Second, the final stage requires domain knowledge for optimal colour threshold selection. They claim that the influence of colour depth on classification results can be mitigated by collecting HER2 slides from multiple centers. In this work, we aim to overcome the limitations and address the criticisms raised in the aforementioned studies. The entire tissue was divided into patches to mimic the pathologist diagnosis. Then, each patch was classified with a score and, according to the rule described in Table 1, the slide is graded automatically. We introduce a novel 7-class approach. The reason is that the classification in some patches is difficult for class 2+. This is the main source of confusion even for pathologists. See Figure 2 for some examples of 2+ patches and how they can be confused with 1 or 3+ samples. In practice, a 2+ grading prediction is usually equivocal and requires additional tests, such as FISH. The consequences of mixing up these cases, providing an inadequate approach, or failing to address them when required, are significant.