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Training a deep learning model with a large annotated dataset is still a dominant paradigm in automatic whole slide images (WSIs) processing for digital pathology. However, obtaining manual annotations is a labor-intensive task, and an error-prone to inter and intra-observer variability. In this study, we offer an online deep learning-based clustering workflow for annotating and analysis of different types of tissues from histopathology images. Inspired by learning and optimal transport theory, our proposed model consists of two stages. In the first stage, our model learns tissue-specific discriminative representations by contrasting the features in the latent space at two levels, the instance- and the clusterlevel. This is done by maximizing the similarities of the projections of positive pairs (views of the same image) while minimizing those of negative ones (views of the rest of the images). In the second stage, our framework extends the standard cross-entropy minimization to an optimal transport problem and solves it using the Sinkhorn-Knopp algorithm to produce the cluster assignments. Moreover, our proposed method enforces consistency between the produced assignments obtained from views of the same image. Our framework was evaluated on three common histopathological datasets: NCT-CRC, LC2500, and Kather STAD. Experiments show that our proposed framework can identify different tissues in annotation-free conditions with competitive results. It achieved an accuracy of 0.9364 in human lung patched WSIs and 0.8464 in images of human colorectal tissues outperforming state of the arts contrastive-based methods.