1 Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, Quebec, H3G 1M8, Canada. Electronic address: cassandre.notton@mail.concordia.ca.
2 Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, Quebec, H3G 1M8, Canada. Electronic address: vasudev.sharma@mail.concordia.ca.
3 University of Montreal, 2900, boul. Edouard-Montpetit, Montreal, Quebec, K3T 1J4, Canada. Electronic address: quoc-huy.trinh@umontreal.ca.
4 Sunnybrook Health Science Centre, 2075 Bayview Avenue, Toronto, Ontario, M4N 3M5, Canada. Electronic address: lina.chen@sunnybrook.ca.
5 Kingston General Hospital, 76 Stuart Street, Kingston, Quebec, K7L 2V7, Canada. Electronic address: minqi.xu@kingstonhsc.ca.
6 Kingston General Hospital, 76 Stuart Street, Kingston, Quebec, K7L 2V7, Canada. Electronic address: Sonal.Varma@kingstonhsc.ca.
7 Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, Quebec, H3G 1M8, Canada; Mila-Quebec Artificial Intelligence Institute, 6666, St-Urbain, #200, Montreal, Quebec, H2S 3H1, Canada. Electronic address: mahdi.hosseini@concordia.ca.

Colorectal cancer (CRC) is one of the few cancers that have an established dysplasia-carcinoma sequence that benefits from screening. Everyone over 50 years of age in Canada is eligible for CRC screening. About 20% of those people will undergo a biopsy for a pre-neoplastic polyp and, in many cases, multiple polyps. As such, these polyp biopsies make up the bulk of a pathologist's workload. Developing an efficient computational model to help screen these polyp biopsies can improve the pathologist's workflow and help guide their attention to critical areas on the slide. Deep Learning (DL) models face significant challenges in computational pathology (CPath) because of the gigapixel image size of whole-slide images and the scarcity of detailed annotated datasets. It is, therefore, crucial to leverage self-supervised learning (SSL) methods to alleviate the burden and cost of data annotation. However, current research lacks methods to apply SSL frameworks to analyze pathology data effectively. This paper aims to propose an optimized Barlow Twins framework for colorectal polyps screening. We adapt its hyperparameters, augmentation strategy and encoder to the specificity of the pathology data to enhance performance. Additionally, we investigate the best Field of View (FoV) for colorectal polyps screening and propose a new benchmark dataset for CRC screening, made of four types of colorectal polyps and normal tissue, by performing downstream tasking on MHIST and NCT-CRC-7K datasets. Furthermore, we show that the SSL representations are more meaningful and qualitative than the supervised ones and that Barlow Twins benefits from the Swin Transformer when applied to pathology data. Codes are available from https://github.com/AtlasAnalyticsLab/PathBT.