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

• URL: http://arxiv.org/abs/2302.05195v2
• Date: Wed, 7 Jun 2023 16:05:44 GMT
• Title: Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime
• Authors: Thomas Stegm\"uller, Christian Abbet, Behzad Bozorgtabar, Holly Clarke, Patrick Petignat, Pierre Vassilakos, and Jean-Philippe Thiran
• Abstract summary: Screening Papanicolaou test samples has proven to be highly effective in reducing cervical cancer-related mortality. Deep learning-based telecytology diagnosis emerges as an appealing alternative, but it requires the collection of large annotated training datasets. 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.
• Score: 8.981411680742973
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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