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。