Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime

TL;DR

This paper introduces a self-supervised learning approach for cervical cytology that leverages unlabeled Pap smear images, improving diagnosis accuracy in resource-limited settings without requiring extensive annotated datasets.

Contribution

It proposes C3P, a novel augmentation method enabling transfer learning from single-cell datasets to whole slide images, enhancing deep learning performance in low-data regimes.

Findings

C3P outperforms naive transfer from single-cell images.

Integration of C3P improves multiple instance learning methods.

Effective diagnosis in low-resource settings using low-cost technologies.

Abstract

Screening Papanicolaou test samples has proven to be highly effective in reducing cervical cancer-related mortality. However, the lack of trained cytopathologists hinders its widespread implementation in low-resource settings. Deep learning-based telecytology diagnosis emerges as an appealing alternative, but it requires the collection of large annotated training datasets, which is costly and time-consuming. In this paper, we demonstrate that the abundance of unlabeled images that can be extracted from Pap smear test whole slide images presents a fertile ground for self-supervised learning methods, yielding performance improvements relative to readily available pre-trained models for various downstream tasks. In particular, we propose \textbf{C}ervical \textbf{C}ell \textbf{C}opy-\textbf{P}asting ($\texttt{C}^{3}\texttt{P}$) as an effective augmentation method, which enables knowledge transfer from open-source and labeled single-cell datasets to unlabeled tiles. Not only does $\texttt{C}^{3}\texttt{P}$ outperforms naive transfer from single-cell images, but we also demonstrate its advantageous integration into multiple instance learning methods. Importantly, all our experiments are conducted on our introduced \textit{in-house} dataset comprising liquid-based cytology Pap smear images obtained using low-cost technologies. This aligns with our objective of leveraging deep learning-based telecytology for diagnosis in low-resource settings.