Whole Slide Images Based Cervical Cancer Classification Using Self-Supervised Learning and Multiple Instance Learning

Most whole-slide picture classification systems now rely on manual pixel-level annotations, which are delicate and time-consuming, and necessitate the annotation of specialized topic expertise. We propose employing self-supervised learning and multiple instance learning to handle large WSI datasets with only the reported diagnoses as labels to address this issue.

Here we a machine learning techniques i.e.  K-means Clustering and the deep neural network i.e. convolutional neural network that showed better results when compared to k-means clustering and the features learning by CNN are better for classification applications.

Keywords: Whole Slide Images, K-means Clustering, Convolutional neural network, Features, Cervical Cancer.