Artificial intelligence neuropathologist for glioma classification using deep learning on hematoxylin and eosin stained slide images and molecular markers,Neuro-Oncology

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Artificial intelligence neuropathologist for glioma classification using deep learning on hematoxylin and eosin stained slide images and molecular markers
Neuro-Oncology ( IF 16.4 ) Pub Date : 2020-07-14 , DOI: 10.1093/neuonc/noaa163
Lei Jin _{1,

2} , Feng Shi ₃ , Qiuping Chun ₃ , Hong Chen ₄ , Yixin Ma _{1,

2} , Shuai Wu _{1,

2} , N U Farrukh Hameed _{1,

2} , Chunming Mei ₅ , Junfeng Lu _{1,

2} , Jun Zhang ₅ , Abudumijiti Aibaidula _{1,

2} , Dinggang Shen ₃ , Jinsong Wu _{1,

2,

6}

Affiliation

Pathological diagnosis of glioma subtypes is essential for treatment planning and prognosis. Standard histological diagnosis of glioma is based on postoperative hematoxylin and eosin stained slides by neuropathologists. With advancing artificial intelligence (AI), the aim of this study was to determine whether deep learning can be applied to glioma classification.

中文翻译：

人工智能神经病理学家利用苏木精和伊红染色玻片图像和分子标记的深度学习进行神经胶质瘤分类

胶质瘤亚型的病理诊断对于治疗计划和预后至关重要。神经胶质瘤的标准组织学诊断基于神经病理学家术后苏木精和伊红染色的载玻片。随着人工智能（AI）的进步，本研究的目的是确定深度学习是否可以应用于神经胶质瘤分类。

更新日期：2020-07-14

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