Machine Learning in Radiomic Renal Mass Characterization: Fundamentals, Applications, Challenges, and Future Directions

被引：18

作者：

Kocak, Burak ^{[1
]}

Kus, Ece Ates ^{[1
]}

Yardimci, Aytul Hande ^{[1
]}

Bektas, Ceyda Turan ^{[1
]}

Kilickesmez, Ozgur ^{[1
]}

机构：

[1] Istanbul Training & Res Hosp, Dept Radiol, TR-34098 Istanbul, Turkey

来源：

AMERICAN JOURNAL OF ROENTGENOLOGY | 2020年 / 215卷 / 04期

关键词：

artificial intelligence; deep learning; machine learning; radiomics; renal cell carcinoma; texture analysis; CT TEXTURE ANALYSIS; CELL CARCINOMA; CLEAR-CELL; DIFFERENTIATION; ANGIOMYOLIPOMA; SOCIETY; IMAGES; FAT; CLASSIFICATION; FEATURES;

D O I：

10.2214/AJR.19.22608

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

OBJECTIVE. The purpose of this study is to provide an overview of the traditional machine learning (ML)-based and deep learning-based radiomic approaches, with focus placed on renal mass characterization. CONCLUSION. ML currently has a very low barrier to entry into general medical practice because of the availability of many open-source, free, and easy-to-use toolboxes. Therefore, it should not be surprising to see its related applications in renal mass characterization. A wider picture of the previous works might be beneficial to move this field forward.

引用

页码：920 / 928

页数：9

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