Unveiling Thymoma Typing Through Hyperspectral Imaging and Deep Learning.

Journal of biophotonics Pub Date : 2024-11-01 Epub Date: 2024-10-03 DOI:10.1002/jbio.202400325
Qize Lv, Ke Liang, ChongXuan Tian, YanHai Zhang, YunZe Li, JinLin Deng, WeiMing Yue, Wei Li
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Abstract

Thymoma, a rare tumor from thymic epithelial cells, presents diagnostic challenges because of the subjective nature of traditional methods, leading to high false-negative rates and long diagnosis times. This study introduces a thymoma classification technique that integrates hyperspectral imaging with deep learning. We initially capture pathological slice images of thymoma using a hyperspectral camera and delineate regions of interest to extract spectral data. This data undergoes reflectance calibration and noise reduction. Subsequently, we transform the spectral data into two-dimensional images via the Gramian Angular Field (GAF) method. A variant residual network is then utilized to extract features and classify these images. Our results demonstrate that this model significantly enhances classification accuracy and efficiency, achieving an average accuracy of 95%. The method proves highly effective in automated thymoma diagnosis, optimizing data utilization, and feature representation learning.

通过高光谱成像和深度学习揭示胸腺瘤类型。
胸腺瘤是一种来自胸腺上皮细胞的罕见肿瘤,由于传统方法的主观性,导致假阴性率高和诊断时间长,给诊断带来了挑战。本研究介绍了一种将高光谱成像与深度学习相结合的胸腺瘤分类技术。我们首先使用高光谱相机捕捉胸腺瘤的病理切片图像,并划定感兴趣区域以提取光谱数据。这些数据经过反射率校准和降噪处理。随后,我们通过格拉米安角场(GAF)方法将光谱数据转换为二维图像。然后利用变异残差网络提取特征并对这些图像进行分类。我们的研究结果表明,该模型大大提高了分类的准确性和效率,平均准确率达到 95%。事实证明,该方法在胸腺瘤自动诊断、优化数据利用和特征表征学习方面非常有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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