Oral cancer has more than 83% survival rate if detected in its early stages, however, only 29% of cases are currently detected early. Deep learning techniques can detect patterns of oral cancer cells and can aid in its early detection. In this work, we present the first results of neural networks for oral cancer detection using microscopic images. We compare numerous state-of-the-art models via transfer learning approach and collect and release an augmented dataset of high-quality microscopic images of oral cancer. We present a comprehensive study of different models and report their performance on this type of data. Overall, we obtain a 10-15% absolute improvement with transfer learning methods compared to a simple Convolutional Neural Network baseline. Ablation studies show the added benefit of data augmentation techniques with finetuning for this task.

翻译：口腔癌如果在早期检测到,其存活率超过83%以上,但是,目前只有29%的病例得到早期检测。深层学习技术可以检测口腔癌细胞的模式,有助于早期检测。在这项工作中,我们展示了使用微型图像进行口腔癌检测的神经网络的第一批结果。我们通过转移学习方法比较了许多最先进的模型,并收集和发布大量高质量的口腔癌微科图像的强化数据集。我们展示了对不同模型的全面研究,并报告了这类数据的业绩。总的来说,我们获得了10-15%的绝对改进,与简单的革命神经网络基线相比,我们获得了转移学习方法的绝对改进。吸收研究显示数据增强技术的附加效益,并对这一任务进行了微调。