Detection of Extremely Sparse Key Instances in Whole Slide Cytology Images via Self-supervised One-class Representation Learning

Swarnadip Chatterjee, Orcun Göksel, Natasa Sladoje, Joakim Lindblad

Abstract: Whole slide pathological image classification using slide-level labels often relies on multiple instance learning. Multiple instance learning based approaches are particularly challenging with whole slide cytology images, where the vast number of instances can make it difficult to identify key instances, especially when they are scarce. In this work we evaluate whether using representations learnt from patches from only normal slides is effective for instance-level decision making. We aim for interpretable slide-level decision making for whole slide cytology images. We focus on the effectiveness of a self-supervised contrastive learning framework within a one-class classifier setting, assessing its ability to learn the appearances of normal cells from a limited number of normal slides and subsequently identify abnormal cells (key instances) on test slides. We evaluate our approach on a publicly available cytology dataset, achieving a Recall@400 score of 0.1938, considerably improving over the 0.1109 score obtained using a weakly supervised approach.