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 over
single-modality methods. Intermediate fusion is the leading method among the
studied approaches. Specifically, the Co-Attention Fusion Network (CAFNet)
model excels with an F1 score of 83.34% and accuracy of 91.79%, surpassing
human performance on the task. Additional tests highlight the need for precise
image registration to optimize multimodal analysis benefits. This study
advances cytopathology by combining deep learning and multimodal imaging to
enhance early, non-invasive detection of oral cancer, improving diagnostic
accuracy and streamlining clinical workflows. The developed pipeline is also
applicable in other cytological settings. Our codes and dataset are available
online for further research.

结合多模态成像和深度融合的方法，该研究旨在提高基于人工智能的口腔癌检测的准确性，通过亚甲基蓝染色液基细胞学刷片的亮场和荧光显微镜成像分析，证实荧光成像提供了重要的诊断信息，并且多模态融合方法提高了单模态方法的分类和癌症检测准确性，其中 Co-Attention Fusion Network（CAFNet）模型在测试中表现优秀，F1 分数达到 83.34％，准确率为 91.79％，超越了人类的表现，进一步测试显示对图像配准的精确性优化有助于优化多模态分析的效果，这项研究通过结合深度学习和多模态成像推动了细胞病理学的发展，提高了口腔癌的早期非侵入性检测的准确性，改进了诊断精度和临床工作流程，并且该研究开发的流程也适用于其他细胞学领域。

发光献艺：巴那科罗染色改进基于人工智能的细胞学口腔癌检测

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

Accurate detection of oral cancer is crucial for improving patient outcomes.
However, the field faces two key challenges: the scarcity of deep
learning-based image segmentation research specifically targeting oral cancer
and the lack of annotated data. Our study proposes OCU-Net, a pioneering U-Net
image segmentation architecture exclusively designed to detect oral cancer in
hematoxylin and eosin (H&E) stained image datasets. OCU-Net incorporates
advanced deep learning modules, such as the Channel and Spatial Attention
Fusion (CSAF) module, a novel and innovative feature that emphasizes important
channel and spatial areas in H&E images while exploring contextual information.
In addition, OCU-Net integrates other innovative components such as
Squeeze-and-Excite (SE) attention module, Atrous Spatial Pyramid Pooling (ASPP)
module, residual blocks, and multi-scale fusion. The incorporation of these
modules showed superior performance for oral cancer segmentation for two
datasets used in this research. Furthermore, we effectively utilized the
efficient ImageNet pre-trained MobileNet-V2 model as a backbone of our OCU-Net
to create OCU-Netm, an enhanced version achieving state-of-the-art results.
Comprehensive evaluation demonstrates that OCU-Net and OCU-Netm outperformed
existing segmentation methods, highlighting their precision in identifying
cancer cells in H&E images from OCDC and ORCA datasets.

准确检测口腔癌对于改善患者预后至关重要。然而，在这一领域面临两个关键挑战：缺少专门针对口腔癌的深度学习图像分割研究和缺乏带注释的数据。我们的研究提出了 OCU-Net，这是一种独特设计的 U-Net 图像分割架构，专门用于检测血红蛋白和嗜酸性染色（H&E）图像数据集中的口腔癌。OCU-Net 融合了先进的深度学习模块，如通道和空间注意力融合 (CSAF) 模块，这是一种强调 H&E 图像中重要通道和空间区域并探索上下文信息的新颖特性。此外，OCU-Net 还集成了其他创新组件，如挤压激励 (SE) 注意力模块、空洞空间金字塔池化 (ASPP) 模块、残差块和多尺度融合。这些模块的结合在本研究中展现出对两个数据集的口腔癌分割的优越性能。此外，我们有效利用了高效的预训练 ImageNet 的 MobileNet-V2 模型作为 OCU-Net 的骨干网络，创建了 OCU-Netm，一个达到最先进结果的增强版本。全面评估表明，OCU-Net 和 OCU-Netm 优于现有的分割方法，在 OCDC 和 ORCA 数据集的 H&E 图像中准确识别癌细胞。

OCU-Net：一种用于增强口腔癌分割的新型 U-Net 架构

OCU-Net: A Novel U-Net Architecture for Enhanced Oral Cancer  Segmentation

This document outlines a PROSPERO pre-registered protocol for a systematic
review regarding articulatory changes in speech following oral or orophayrngeal
cancer treatment. Treatment of tumours in the oral cavity may result in
physiological changes that could lead to articulatory difficulties. The tongue
becomes less mobile due to scar tissue and/or potential (postoperative)
radiation therapy. Moreover, tissue loss may create a bypass for airflow or
limit constriction possibilities. In order to gain a better understanding of
the nature of the speech problems, information regarding the movement of the
articulators is needed since perceptual or acoustic information provide only
indirect evidence of articulatory changes. Therefore, this systematic review
will review studies that directly measured the articulatory movements of the
tongue, jaw, and lips following treatment for oral or oropharyngeal cancer.

本文介绍了一项针对口腔或咽部癌症治疗后言语障碍的系统性审查方案，以直接测量患者口腔各器官运动作为研究焦点，以期获得更好的关于口腔癌症治疗后言语问题的认识。