A new auto-annotation method and machine learning strategy for detection and annotation of cancer areas in prostate biopsies

Bjork L.; Gustafsson J.; Noraddin F. Hikmet; Euren K.; Lindskog C.

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A new auto-annotation method and machine learning strategy for detection and annotation of cancer areas in prostate biopsies

机译：一种新的自动注释方法和机器学习策略，用于检测和注释前列腺活组织检查

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来源
《Virchows Archiv: an international journal of pathology》 |2019年第1期|共2页
作者
Bjork L.; Gustafsson J.; Noraddin F. Hikmet; Euren K.; Lindskog C.;
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作者单位

Karolinska Inst Stockholm Sweden;

Uppsala Univ Uppsala Sweden;

Uppsala Univ Uppsala Sweden;

Contextvision Linkoping Sweden;

Uppsala Univ Uppsala Sweden;

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原文格式 PDF
正文语种 eng
中图分类医学微生物学（病原细菌学、病原微生物学）;
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1. A new auto-annotation method and machine learning strategy for detection and annotation of cancer areas in prostate biopsies [J] . Bjork L., Gustafsson J., Noraddin F. Hikmet, Virchows Archiv: an international journal of pathology . 2019,第Suppla1期

机译：一种新的自动注释方法和机器学习策略，用于检测和注释前列腺活组织检查
2. Evaluation of a prostate biopsy strategy for cancer detection using a computer simulation system with virtual needle biopsy for three-dimensional prostate models. [J] . Noguchi M, Deguchi D, Toriwaki J, International journal of urology: official journal of the Japanese Urological Association . 2006,第10期

机译：使用带有虚拟针头活检的计算机仿真系统对三维前列腺模型进行前列腺癌活检策略的癌症检测评估。
3. Machine learning methods can more efficiently predict prostate cancer compared with prostate-specific antigen density and prostate-specific antigen velocity [J] . Satoshi Nitta, Masakazu Tsutsumi, Shotaro Sakka, Prostate International . 2019,第3期

机译：与前列腺特异性抗原密度和前列腺特异性抗原速度相比，机器学习方法可以更有效地预测前列腺癌
4. Statistical atlases and machine learning tools applied to optimized prostate biopsy for cancer detection and estimation of volume and Gleason score [C] . Davatzikos Christos 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro . 2011

机译：统计地图集和机器学习工具应用于优化的前列腺活检，用于癌症检测以及体积和格里森评分的估计
5. Optimization and Machine Learning Methods for Diagnostic Testing of Prostate Cancer [D] . Merdan, Selin 2018

机译：前列腺癌诊断测试的优化和机器学习方法
6. Machine learning methods can more efficiently predict prostate cancer compared with prostate-specific antigen density and prostate-specific antigen velocity [O] . Satoshi Nitta, Masakazu Tsutsumi, Shotaro Sakka, 2019

机译：与前列腺特异性抗原密度和前列腺特异性抗原速度相比机器学习方法可以更有效地预测前列腺癌
7. Repeat prostate biopsy strategies after initial negative biopsy: meta-regression comparing cancer detection of transperineal, transrectal saturation and MRI guided biopsy. [O] . Adam W Nelson, Rebecca C Harvey, Richard A Parker, 2013

机译：在最初的阴性活检后重复前列腺活检策略：meta-regression比较经会阴，经直肠饱和和mRI引导活检的癌症检测。

A new auto-annotation method and machine learning strategy for detection and annotation of cancer areas in prostate biopsies

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