Improving the Subtype Classification of Non-small Cell Lung Cancer by Elastic Deformation Based Machine Learning.
Non-invasive image-based machine learning models have been used to classify subtypes of non-small cell lung cancer (NSCLC). However, the classification performance is limited by the dataset size, because insufficient data cannot fully represent the characteristics of the tumor lesions. In this work,...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 3; pp. 605 - 618 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151702164&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151702164 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2021 vid: 34 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 151702164 150193506 151702164 151702164 10.1007/s10278-021-00455-0 151702164 ppf: 605 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Improving the Subtype Classification of Non-small Cell Lung Cancer by Elastic Deformation Based Machine Learning. aug: au: Gao, Yang Song, Fan Zhang, Peng Liu, Jian Cui, Jingjing Ma, Yingying Zhang, Guanglei Luo, Jianwen affil: Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China sug: subj: Carcinoma, Non-Small-Cell Lung Classification Diagnostic Imaging Machine Learning Image Processing, Computer Assisted Elasticity Carcinoma, Non-Small-Cell Lung Diagnosis Human ROC Curve Sensitivity and Specificity Neural Networks (Computer) Funding Source ab: Non-invasive image-based machine learning models have been used to classify subtypes of non-small cell lung cancer (NSCLC). However, the classification performance is limited by the dataset size, because insufficient data cannot fully represent the characteristics of the tumor lesions. In this work, a data augmentation method named elastic deformation is proposed to artificially enlarge the image dataset of NSCLC patients with two subtypes (squamous cell carcinoma and large cell carcinoma) of 3158 images. Elastic deformation effectively expanded the dataset by generating new images, in which tumor lesions go through elastic shape transformation. To evaluate the proposed method, two classification models were trained on the original and augmented dataset, respectively. Using augmented dataset for training significantly increased classification metrics including area under the curve (AUC) values of receiver operating characteristics (ROC) curves, accuracy, sensitivity, specificity, and f1-score, thus improved the NSCLC subtype classification performance. These results suggest that elastic deformation could be an effective data augmentation method for NSCLC tumor lesion images, and building classification models with the help of elastic deformation has the potential to serve for clinical lung cancer diagnosis and treatment design. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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