Breast histopathology using random decision forests-based classification of infrared spectroscopic imaging data
Breast histopathology using random decision forests-based classification of infrared spectroscopic imaging data
复制标题
使用基于随机决策森林的红外光谱成像数据分类进行乳腺组织病理学
DOI：
10.1117/12.2043783
复制
发表时间：
2014
影响因子：
7.4
通讯作者：
R. Bhargava
中科院分区：
文献类型：
--
作者：
D. Mayerich;M. Walsh;Andre Kadjacsy;Shachi Mittal;R. Bhargava
目前的癌症检测方法依赖于临床染色，通常使用免疫组织化学技术。病理学家然后评估染色组织，以确定癌症阶段的治疗方案。这些方法是常用的，但它们是非定量的，难以控制染色质量。在本文中，我们提出使用中红外光谱成像来分类肿瘤活检样本中的组织类型。我们的目标是通过向病理学家提供定量的化学信息来增加他们可用的数据，以帮助与乳腺癌相关的临床和研究活动的诊断活动。
Current methods for cancer detection rely on clinical stains, often using immunohistochemistry techniques. Pathologists then evaluate the stained tissue in order to determine cancer stage treatment options. These methods are commonly used, however they are non-quantitative and it is difficult to control for staining quality. In this paper, we propose the use of mid-infrared spectroscopic imaging to classify tissue types in tumor biopsy samples. Our goal is to augment the data available to pathologists by providing them with quantitative chemical information to aid diagnostic activities in clinical and research activities related to breast cancer.