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作者

Chongxuan Tian Yue Xi Yuting Ma Cai Chen Cong Wu Kun Ru Wei Li & …Miaoqing Zhao

作者单位

¹School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China ³Department of Pathology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China ²Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, Shandong, China ⁴Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan, Shandong, China

刊名

Journal of Imaging Informatics in Medicine

年份

2025

卷号

Vol.38 No.2

页码

1098-1111

关键词

Primary central nervous system diffuse large B-cell lymphoma High-grade glioma Pathological diagnosis Computer-aided diagnosis CNN Transfer learning

摘要

Primary diffuse central nervous system large B-cell lymphoma and high-grade glioma often present similarly, clinically and on imaging, making differentiation challenging. This similarity can complicate pathologists’ diagnostic efforts, yet accurately distinguishing between these conditions is crucial for guiding treatment decisions. This study leverages a deep learning model to classify brain tumor pathology images, addressing the common issue of limited medical imaging data. Instead of traini...更多

Primary diffuse central nervous system large B-cell lymphoma and high-grade glioma often present similarly, clinically and on imaging, making differentiation challenging. This similarity can complicate pathologists’ diagnostic efforts, yet accurately distinguishing between these conditions is crucial for guiding treatment decisions. This study leverages a deep learning model to classify brain tumor pathology images, addressing the common issue of limited medical imaging data. Instead of training a convolutional neural network from scratch, we employ a pre-trained network for extracting deep features, which are then used by a support vector machine for classification. Our evaluation shows that the Resnet50 model achieves a 97.4% accuracy, based on tenfold cross-validation on the test set. These results highlight the synergy between deep learning and traditional diagnostics, potentially setting a new standard for accuracy and efficiency in the pathological diagnosis of brain tumors.收起

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