首页|Findings from Beihang University Reveals New Findings on Machine Learning (A Novel Feature Engineering Method Based On Latent Representation Learning for Radiomics: Application In Nsclc Sub- type Classification)
Findings from Beihang University Reveals New Findings on Machine Learning (A Novel Feature Engineering Method Based On Latent Representation Learning for Radiomics: Application In Nsclc Sub- type Classification)
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NETL
NSTL
IEEE
Researchers detail new data in Machine Learning. According to news reporting from Beijing, People's Republic of China, by NewsRx journalists, research stated, "Radiomics refers to the high-throughput extraction of quantitative features from medical images, and is widely used to construct machine learning models for the prediction of clinical outcomes, while feature engineering is the most important work in radiomics. However, current feature engineering methods fail to fully and effectively utilize the heterogeneity of features when dealing with different kinds of radiomics features." Financial support for this research came from National Natural Science Foundation of China (NSFC). The news correspondents obtained a quote from the research from Beihang University, "In this work, latent representation learning is first presented as a novel feature engineering approach to reconstruct a set of latent space features from original shape, intensity and texture features. This proposed method projects features into a subspace called latent space, in which the latent space features are obtained by minimizing a unique hybrid loss function including a clustering-like loss and a reconstruction loss. The former one ensures the separability among each class while the latter one narrows the gap between the original features and latent space features. Experiments were performed on a multi-center non-small cell lung cancer (NSCLC) subtype classification dataset from 8 international open databases. Results showed that compared with four traditional feature engineering methods (baseline, PCA, Lasso and L2,1-norm minimization), latent representation learning could significantly improve the classification performance of various machine learning classifiers on the independent test set (all p＜0.001). Further on two additional test sets, latent representation learning also showed a significant improvement in generalization performance."
BeijingPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesEngineeringMachine LearningBeihang University
NETL
NSTL
IEEE
