## 1 Introduction  
Malignant tumors of the head and neck region include a large variety of lesions, the great majority of which are squamous cell carcinomas of the oral cavity [1]. According to GLOBOCAN 2018 data on cancer [2], oral cavity malignant neoplasms, together with lip and pharynx malignancies, account for more than half-million new occurrences per year worldwide, with an estimated incidence of 5.06 cases per 100,000 inhabitants. Moreover, Oral Squamous Cell Carcinoma (OSCC) is characterized by high morbidity and mortality, and, in most countries, the survival rate after five years from the diagnosis is less than 50% of the patients [3].  
The histology examination is the gold standard for the definition of these tumors. Surgical pathologists use both clinical and radiological evidence to complement their diagnoses, differentiating between benign and malignant lesions. In the last years, surgical pathology is witnessing a digital transformation thanks to (1) the increase of the processing speed of Whole Slide Images (WSI) scanners [4] and (2) the lower storage costs and better compression algorithm [5]. Consequently, WSI digital analysis is one of the most prominent and innovative topics in anatomical pathology, catching academic and industries attentions. An example of an image obtained using a WSI scanner is shown in Figure 1.  
However, WSI (and associated datasets) are characterized by three important limitations:  
1. WSI are extremely large images, having a memory size of two gigabytes on average [6].
2. There are a few surgical pathology units that are fully digitalized and that can store a large amount of digitalized slides, although their number is increasing exponentially [7].
3. There is a small number of available image datasets and most of them are not annotated [8].  
Due to the above-discussed limitations, the research activity based on Artificial Intelligence (AI) algorithms applied to WSI is still limited compared to other diagnostic imaging branches, such as radiology, but the scientific literature on the topic is growing fast and we are observing the appearance of public datasets of unannotated and annotated histopathology WSI.  
In this paper, we present a performance evaluation of four different image segmentation architectures based on deep learning to obtain a pixel-wise separation between benign and malignant areas on WSI samples. In particular, we test four widely used Semantic Segmentation deep neural Networks (SSNs) on publicly available data for the detection of carcinomas. As a difference with respect to classification neural network, SSNs take as input images of arbitrary sizes and produce a correspondingly sized segmented output, without relying on local patches.  
The contributions of this work are three-fold:  
1. We compare four different supervised pixel-wise segmentation methods for detecting carcinoma areas in WSI using quantitative metrics. Different input formats, including separating the color channels in the RGB and Hue, Saturation, and Value (HSV) models, are taken into account in the experiments.
2. We use two different image datasets, one for training and another one for testing. This allows us to understand the real generalization capabilities of the considered SSNs.
3. We created a publicly available dataset, called Oral Cancer Annotated (ORCA) dataset, containing annotated data from the Cancer Genome Atlas (TCGA) dataset, which can be used by other researchers for testing their approaches.  
The paper is organized as follows. Section 2 contains a discussion of similar approaches present in the literature. Section 3 describes the details of the proposed method, while Section 4 shows both qualitative and quantitative results obtained on publicly available data. Finally, conclusions are drawn in Section 5.  
Figure 1: An example of image generated by a Whole Slide Images (WSI) scanner. The image has a dimension of 35,862 \(\times\) 32,195 pixels and the file size is 213.3 MB.