It can be challenging to detect tumor margins during surgery for complete resection. The purpose of this work is to develop a novel learning method that learns the difference between the tumor and benign tissue adaptively for cancer detection on hyperspectral images in an animal model. Specifically, an auto-encoder network is trained based on the wavelength bands on hyperspectral images to extract the deep information to create a pixel-wise prediction of cancerous and benign pixel. According to the output hypothesis of each pixel, the misclassified pixels would be reclassified in the right prediction direction based on their adaptive weights. The auto-encoder network is again trained based on these updated pixels. The learner can adaptively improve the ability to identify the cancer and benign tissue by focusing on the misclassified pixels, and thus can improve the detection performance. The adaptive deep learning method highlighting the tumor region proved to be accurate in detecting the tumor boundary on hyperspectral images and achieved a sensitivity of 92.32% and a specificity of 91.31% in our animal experiments. This adaptive learning method on hyperspectral imaging has the potential to provide a noninvasive tool for tumor detection, especially, for the tumor whose margin is indistinct and irregular.

中文翻译：

---

使用高光谱成像进行头颈癌检测的自适应深度学习

在手术过程中检测肿瘤边缘以进行完全切除可能具有挑战性。这项工作的目的是开发一种新的学习方法，该方法可以自适应地学习肿瘤和良性组织之间的差异，以在动物模型的高光谱图像上进行癌症检测。具体来说，自动编码器网络基于高光谱图像上的波段进行训练，以提取深度信息，以创建癌性和良性像素的逐像素预测。根据每个像素的输出假设，错误分类的像素将根据其自适应权重在正确的预测方向上重新分类。自动编码器网络再次基于这些更新的像素进行训练。学习者可以通过关注错误分类的像素来自适应地提高识别癌症和良性组织的能力，从而可以提高检测性能。强调肿瘤区域的自适应深度学习方法被证明在检测高光谱图像上的肿瘤边界方面是准确的，在我们的动物实验中实现了 92.32% 的灵敏度和 91.31% 的特异性。这种高光谱成像的自适应学习方法有可能为肿瘤检测提供一种无创工具，特别是对于边缘不清晰和不规则的肿瘤。