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本文引用的文献

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Stroma classification for neuroblastoma on graphics processors.基于图形处理器的神经母细胞瘤基质分类
Int J Data Min Bioinform. 2009;3(3):280-98. doi: 10.1504/ijdmb.2009.026702.
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Towards improved cancer diagnosis and prognosis using analysis of gene expression data and computer aided imaging.通过基因表达数据分析和计算机辅助成像改善癌症诊断与预后
Exp Biol Med (Maywood). 2009 Aug;234(8):860-79. doi: 10.3181/0902-MR-89. Epub 2009 Jun 2.
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Adaptive discriminant wavelet packet transform and local binary patterns for meningioma subtype classification.用于脑膜瘤亚型分类的自适应判别小波包变换与局部二值模式
Med Image Comput Comput Assist Interv. 2008;11(Pt 2):196-204. doi: 10.1007/978-3-540-85990-1_24.
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An illustration of the potential for mapping MRI/MRS parameters with genetic over-expression profiles in human prostate cancer.一幅关于在人类前列腺癌中绘制MRI/MRS参数与基因过表达谱之间关系潜力的示意图。
MAGMA. 2008 Nov;21(6):411-21. doi: 10.1007/s10334-008-0133-3. Epub 2008 Aug 28.
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Computerized pathological image analysis for neuroblastoma prognosis.用于神经母细胞瘤预后评估的计算机化病理图像分析
AMIA Annu Symp Proc. 2007 Oct 11;2007:304-8.
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Computer-assisted grading of neuroblastic differentiation.神经母细胞分化的计算机辅助评分
Arch Pathol Lab Med. 2008 Jun;132(6):903-4; author reply 904. doi: 10.5858/2008-132-903-CGOND.
7
Interobserver reproducibility of Gleason grading: evaluation using prostate cancer tissue microarrays.Gleason分级的观察者间可重复性：使用前列腺癌组织微阵列进行评估
J Cancer Res Clin Oncol. 2008 Oct;134(10):1071-8. doi: 10.1007/s00432-008-0388-0. Epub 2008 Apr 8.
8
Multifeature prostate cancer diagnosis and Gleason grading of histological images.组织学图像的多特征前列腺癌诊断与Gleason分级
IEEE Trans Med Imaging. 2007 Oct;26(10):1366-78. doi: 10.1109/TMI.2007.898536.
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Detecting prostatic adenocarcinoma from digitized histology using a multi-scale hierarchical classification approach.使用多尺度分层分类方法从数字化组织学中检测前列腺腺癌。
Conf Proc IEEE Eng Med Biol Soc. 2006;2006:4759-62. doi: 10.1109/IEMBS.2006.260188.
10
Reproducibility and reliability of tumor grading in urological neoplasms.泌尿系统肿瘤中肿瘤分级的可重复性和可靠性。
World J Urol. 2007 Dec;25(6):595-605. doi: 10.1007/s00345-007-0209-0. Epub 2007 Sep 9.

Digital pathology image analysis: opportunities and challenges.

作者信息

Madabhushi Anant

出版信息

Imaging Med. 2009;1(1):7-10. doi: 10.2217/IIM.09.9.

DOI:10.2217/IIM.09.9
PMID:30147749
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6107089/
Abstract
摘要