基于神经网络的显微高光谱乳腺癌组织图像研究

目的 探索神经网络结合显微高光谱成像识别乳腺癌组织的可行性和应用价值。方法 采用显微高光谱成像技术采集乳腺癌组织的图像数据，使用基于神经网络的显微高光谱乳腺癌组织图像分析方法，实现乳腺癌组织的自动分类和区域划分。提出数据预处理方法以提高图像的信噪比，利用神经网络训练图谱信息识别乳腺组织病变区域并突显以利于可视化。结果 基于神经网络的显微高光谱的乳腺组织识别分析方法同时利用了图谱两个方面的特征，获得了比传统彩色病理图像更好的识别结果。结论 基于神经网络的显微高光谱乳腺组织图像分析方法可以提供特征性的样本信息，是传统彩色病理图像的有效补充。在神经网络分析方法的支持下，将显微高光谱成像技术应用于乳腺癌组织的分析具有一定的应用前景。

Micro-hyperspectral breast cancer tissue image analysis based on neural network

Objective To explore the feasibility and value of neural network combined with micro-hyperspectral imaging in identifying breast cancer tissue.Methods The micro-hyperspectral imaging technology was used to collect image data of breast cancer tissue, and the micro-hyperspectral breast tissue image analysis method based on neural network was used to realize the automatic classification and regional division of breast cancer tissue. Meanwhile, data preprocessing method was proposed to improve the signal to noise ratio of the image, and map information was trained by neural network to identify breast tissue lesions and highlight them for visualization.Results The micro-hyperspectral breast tissue image analysis method based on neural network utilized two characteristics of the images at the same time, and it was better than traditional color pathological images in identifying breast tissue.Conclusion The micro-hyperspectral breast tissue image analysis method based on neural network can provide more characteristic sample information compared with traditional color pathology images, and may serve as an effective complement to traditional color pathological images. With the support of neural network, the micro-hyperspectral imaging technology has prospects in analyzing breast cancer tissue.