论文标题
卵巢癌组织活检样品的高光谱图像中的淋巴细胞分类
Lymphocyte Classification in Hyperspectral Images of Ovarian Cancer Tissue Biopsy Samples
论文作者
论文摘要
当前诊断患者多种类型癌症进展的方法依赖于解释染色的针头活检。这个过程是耗时的，并且在整个石质化，苏木精和曙红（H＆E）染色，去甲酰化和注释阶段中易受误差。傅立叶变换红外（FTIR）成像已被证明是适当注释活检核心染色的有希望的替代方法，而无需使用傅立叶变换红外（FTIR）图像，而与机器学习以解释密集的光谱信息时，使用傅立叶变换红外（FTIR）图像。我们提出了一条机器学习管道，以在活检核心的高光谱图像中分段白细胞（淋巴细胞）像素。这些细胞对于诊断在临床上很重要，但是由于难以获得精确的像素标签，一些先前的工作一直在努力合并它们。评估的方法包括支持向量机（SVM），高斯天真贝叶斯和多层感知器（MLP），以及分析相对现代的卷积神经网络（CNN）。
Current methods for diagnosing the progression of multiple types of cancer within patients rely on interpreting stained needle biopsies. This process is time-consuming and susceptible to error throughout the paraffinization, Hematoxylin and Eosin (H&E) staining, deparaffinization, and annotation stages. Fourier Transform Infrared (FTIR) imaging has been shown to be a promising alternative to staining for appropriately annotating biopsy cores without the need for deparaffinization or H&E staining with the use of Fourier Transform Infrared (FTIR) images when combined with machine learning to interpret the dense spectral information. We present a machine learning pipeline to segment white blood cell (lymphocyte) pixels in hyperspectral images of biopsy cores. These cells are clinically important for diagnosis, but some prior work has struggled to incorporate them due to difficulty obtaining precise pixel labels. Evaluated methods include Support Vector Machine (SVM), Gaussian Naive Bayes, and Multilayer Perceptron (MLP), as well as analyzing the comparatively modern convolutional neural network (CNN).
