Can I read Let it shine: Autofluorescence of Papanicolaou-stain improves AI-based cytological oral cancer detection on EtoBox?

Let it shine: Autofluorescence of Papanicolaou-stain improves AI-based cytological oral cancer detection by Lian, Wenyi; Lindblad, Joakim; Stark, Christina Runow; Hirsch, Jan-Michaél; Sladoje, Nataša is a scholarly article available to read on EtoBox.

What is Let it shine: Autofluorescence of Papanicolaou-stain improves AI-based cytological oral cancer detection about?

Oral cancer is a global health challenge. It is treatable if detected early, but it is often fatal in late stages. There is a shift from the invasive and time-consuming tissue sampling and histological examination, toward non-invasive brush biopsies and cytological examination. Reliable computer-assisted methods are essential for cost-effective and accurate cytological analysis, but the lack of detailed cell-level annotations impairs model effectiveness. This study aims to improve AI-based oral cancer detection using multimodal imaging and deep fusion. We combine brightfield and fluorescence whole slide microscopy imaging to analyze Papanicolaou-stained liquid-based cytology slides of brush biopsies collected from both healthy and cancer patients. Due to limited cytological annotations, we utilize a weakly supervised deep learning approach using only patient-level labels. We evaluate various multimodal fusion strategies, including early, late, and three recent intermediate fusion methods. Our results show: (i) fluorescence imaging of Papanicolaou-stained samples provides substantial diagnostic information; (ii) multimodal fusion enhances classification and cancer detection accuracy

Author

Lian, Wenyi; Lindblad, Joakim; Stark, Christina Runow; Hirsch, Jan-Michaél; Sladoje, Nataša

Published

2024

Language

EN