Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows

被引：27

作者：

Cobo, Miriam ^{[1
]}

Fernandez-Miranda, Pablo Menendez ^{[2
]}

Bastarrika, Gorka ^{[2
]}

Iglesias, Lara Lloret ^{[1
]}

机构：

[1] CSIC UC, Inst Phys Cantabria IFCA, Adv Comp & E Sci Res Grp, Santander, Spain

[2] Clin Univ Navarra, Dept Radiol, Pamplona, Spain

来源：

SCIENTIFIC DATA | 2023年 / 10卷 / 01期

关键词：

D O I：

10.1038/s41597-023-02641-x

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Recent advances in computer-aided diagnosis, treatment response and prognosis in radiomics and deep learning challenge radiology with requirements for world-wide methodological standards for labeling, preprocessing and image acquisition protocols. The adoption of these standards in the clinical workflows is a necessary step towards generalization and interoperability of radiomics and artificial intelligence algorithms in medical imaging.

引用

页数：7

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