.png){kind=icon}
返回
Multi-Objective-Based Radiomic Feature Selection for Lesion Malignancy Classification
.png){kind=icon}
OA
AI
DOI：10.1109/JBHI.2019.2902298.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Objective: accurately classifying the malignancy of lesions detected in a screening scan is critical for reducing false positives. Radiomics holds great potential to differentiate malignant from benign tumors by extracting and analyzing a large number of quantitative image features. Since not all radiomic features contribute to an effective classifying model, selecting an optimal feature subset is critical. Methods: this work proposes a new multi-objective based feature selection (MO-FS) algorithm that considers sensitivity and specificity simultaneously as the objective functions during feature selection. For MO-FS, we developed a modified entropy-based termination criterion that stops the algorithm automatically rather than relying on a preset number of generations. We also designed a solution selection methodology for multi-objective learning that uses the evidential reasoning approach (SMOLER) to automatically select the optimal solution from the Pareto-optimal set. Furthermore, we developed an adaptive mutation operation to generate the mutation probability in MO-FS automatically. Results: we evaluated the MO-FS for classifying lung nodule malignancy in low-dose CT and breast lesion malignancy in digital breast tomosynthesis. Conclusion: the experimental results demonstrated that the feature set selected by MO-FS achieved better classification performance than features selected by other commonly used methods. Significance: the proposed method is general and more effective radiomic feature selection strategy.
Keyword:
Feature extraction
Lesions
Entropy
Informatics
Sociology
Statistics
Linear programming
Radiomics
lesion malignancy classification
feature selection
multi-objective evolutionary algorithm
evidential reasoning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.8
论文数:
4.6K
被引数:
2.0W
机构
引用论文
分享收藏Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach使用定量放射组学方法通过无创成像解码肿瘤表型.png)
NATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏分享收藏The Lung Image Database Consortium, (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans肺图像数据库联盟 (LIDC) 和图像数据库资源倡议 (IDRI): ct扫描上完整的肺结节参考数据库MEDICAL PHYSICS
OAAI
MEDICAL PHYSICSIF3.2
OAAI分享收藏
查看更多内容