increasing incidence trends, the mortality rate has significantly decreased. Among other factors, this improvement results from better treatment strategies that can be delineated from the assessment of histopathological characteristics [1; 2]. The analysis of tissue sections of cancer specimens (Figure 1) obtained by preoperative biopsy, commonly starts with haematoxylin and eosin (H&E) staining, which is usually followed by immunohistochemistry (IHC), a more advanced staining technique, used to highlight the presence of specific protein receptors [3]. In fact, according to the current guidelines [4], Human epidermal growth factor receptor 2 (HER2) quantification must be routinely tested in all patients with invasive BCa, recurrence cases, and metastatic tumours. The overexpression of this receptor is observed in 10-20% [4] of BCa cases and has been associated with aggressive clinical behaviour and poor prognosis [5]. However, patients diagnosed with HER2-positive BCa have a better response to targeted therapies and consequent improvements in healing and overall survival, which emphasizes the importance of an accurate evaluation of HER2 status [5; 6]. The current guidelines [7], revised by the American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP), in 2018, indicate the following scoring criteria for HER2 IHC:  
* IHC 0+: no staining or incomplete, barely perceptible membrane staining in 10% of tumour cells or less;
* IHC 1+: incomplete, barely perceptible membrane staining in more than 10% of tumour cells;
* IHC 2+: weak to moderate complete membrane staining in more than 10% of tumour cells;
* IHC 3+: circumferential, complete, intense membrane staining in more than 10% of tumour cells. Moreover, cases scoring 0+ or 1+ are classified as HER2 negative, while cases with a score of 3+ are classified as HER2 positive. Cases with score 2+ are classified as equivocal and are further assessed by in situ hybridization (ISH), to test for gene amplification (see Figure 1). In these cases, the HER2 status is given by the ISH result [7]. At the moment, besides very well differentiated tumours, with low nuclear/cytoplasm area ratio, which typically are hormonal driven and therefore generally not positive for HER2, there are no morphological features on H&E slides that allow a reliable prediction of the HER2 status. Therefore, the standard procedure is to perform an additional immunohistochemical study, with additional molecular study in case of equivocal results. Despite the efficiency of IHC and ISH, the additional cost and time spent on these tests might be avoided if all the information needed to infer the HER2 status could be extracted only from H&E whole slide images (WSI), as a preliminary indication of the IHC result. However, to the extent of our knowledge, the task of predicting HER2 status on H&E stained slides has not yet been addressed in the literature, except for a recent challenge: ECDP200's HEROHE Challenge [9]. In this paper we propose a method using a convolutional neural network (CNN), inspired by multiple-instance learning (MIL), to automatically identify the HER2 status on BCa H&E stained slides. To deal with the sheer dimensions of the slides, tiles are extracted from the original images and separately processed by the model, which learns to aggregate the individual tile predictions into a single, image-wide label. Moreover, to introduce some prior knowledge about the morphology of  
Figure 1: Schema of the process of BCa HER2 evaluation, involving H&E staining, IHC testing and, in specific cases, ISH testing. The proposed method aims to evaluate HER2 using only H&E stained slide images. Image examples were adapted from [8]. tissue structures into the model, the CNN has been pre-trained with HER2 IHC stained slides (example in Figure 2a) from the HER2 Scoring Contest (HER2 SC) training set [10].