## 1 Introduction  
Colorectal Cancer (CRC) represents a 10% of overall new cases and presents higher incidence rate in developed countries [1] and could be considered a \"lifestyle\" disease associated with a diet high in calories and animal fat, and sedentarism [2]. In the United States, it has increased from over 132,000 estimated new cases and nearly 50,000 estimated deaths in 2015 [3] to 147,000 estimated new cases and 53,200 estimated deaths in 2020, being the third most commonly diagnosed cancer type [4]. Despite this, CRC detection increases the 5-year survival rate from 18% to 88.5% if diagnosed at an early stage. Furthermore, screening programs allow for detection before the appearance of symptoms so up to 22% of symptomatic cases could be treated earlier [5]. Colonoscopy is the gold standard procedure for detection and treatment of colorectal lesions, and its efficacy is related to the Adenoma Detection Rate (ADR) of the endoscopist, defined as the percentage of colonoscopies with at least one adenoma identified [6]. It is shown that higher ADR is associated with lower interval CRC rates [7], and that flat/sessile and small lesions are frequently more missed than pedunculated/sub-pedunculated and large ones [8,9]. Therefore, different approaches might be followed to improve ADR and reduce the number of missed lesions. During colonoscopy, these approaches include endoscope caps, positional manoeuvres, as well as the use of imaging modalities such as narrow-band imaging (NBI) which emphasize the capillary pattern and mucosa surface which emphasize the capillary pattern and mucosa surface [10]. It is clear that further development of Computer Assisted Diagnosis (CAD) systems is justified thanks to their potential to eventually improve the patient outcome [11]. In recent years, artificial intelligence (AI) and deep learning (DL) [12] have significantly contributed to the field of medical imaging analysis [13, 14, 15] and the use of AI in colonoscopy has showed promising results to increase ADR, although it should be further evaluated in more randomized controlled trials [16]. In the same trend, DL has also boosted the appearance of methods for polyp detection, localisation and segmentation, where end-to-end methods based on convolutional neural networks accompanied by data augmentation strategies are frequently used [17]. Nevertheless, methods for polyp detection are currently more advanced than methods for polyp classification, which might be due to a lack of available datasets for this task [18]. Lastly, it is important to mention that the availability of high-quality endoscopic images and the increased understanding of the technology by the endoscopists are two important factor for the further development of DL for endoscopy [19]. All deep learning approaches rely on a dataset based from which features can be learnt. If the training method is supervised, then the dataset should be labelled. Alternatively, semi-supervised training makes the most of labelled and unlabelled datasets. In the case of medical imaging datasets [20], data are hard to retrieve from healthcare systems, so it is necessary to obtain ethical approval and differently to labelling natural images, manually annotation of medical images is a cumbersome, time-consuming process that requires expert knowledge [21]. Especially when segmentation is the target, two main limitations are also usually recognized: scarce annotations and weak annotations [22]. Therefore, labelled datasets are usually smaller than non-labelled datasets, but in both cases, they are difficult to obtain, and, in many cases, they are proprietary datasets not publicly available for the research community. There are currently some publicly available datasets of colonoscopy images that can be used for polyp detection, localisation and segmentation [17,18]. A general trend is that the more precise the annotation is, the smaller the dataset size is. In the matter at hand, annotation of manual precise binary masks is more time demanding than just labelling frames.