Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime.
Fiche du document
- doi: 10.1016/j.compbiomed.2023.107809
- issn: 0010-4825
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compbiomed.2023.107809
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/pmid/38113684
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/eissn/1879-0534
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_B0ABE91A74B04
info:eu-repo/semantics/openAccess , CC BY 4.0 , https://creativecommons.org/licenses/by/4.0/
Sujets proches
Cellular biology Cell biologyCiter ce document
T. Stegmüller et al., « Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime. », Serveur académique Lausannois, ID : 10.1016/j.compbiomed.2023.107809
Métriques
Partage / Export
Résumé
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-assisted 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 compared to off-the-shelf pre-trained models for various downstream tasks. In particular, we propose Cervical Cell Copy-Pasting (C 3 P) as an effective augmentation method, which enables knowledge transfer from public and labeled single-cell datasets to unlabeled tiles. Not only does C 3 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 in-house dataset comprising liquid-based cytology Pap smear images obtained using low-cost technologies. This aligns with our long-term objective of deep learning-assisted telecytology for diagnosis in low-resource settings.
