Predicting Unnecessary Nodule Biopsies from a Small, Unbalanced, and Pathologically Proven Dataset by Transfer Learning.
This study explores an automatic diagnosis method to predict unnecessary nodule biopsy from a small, unbalanced, and pathologically proven database. The automatic diagnosis method is based on a convolutional neural network (CNN) model. Because of the small and unbalanced samples, the presented metho...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 3; pp. 685 - 697 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2020
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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=143476529&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143476529 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2020 vid: 33 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143476529 143476529 143476529 10.1007/s10278-019-00306-z 143476529 ppf: 685 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predicting Unnecessary Nodule Biopsies from a Small, Unbalanced, and Pathologically Proven Dataset by Transfer Learning. aug: au: Han, Fangfang Yan, Linkai Chen, Junxin Teng, Yueyang Chen, Shuo Qi, Shouliang Qian, Wei Yang, Jie Moore, William Zhang, Shu Liang, Zhengrong affil: School of Biomedical Engineering, Southern Medical University, 510515, Guangzhou, Guangdong, People's Republic of China sug: subj: Neural Networks (Computer) Machine Learning Image Processing, Computer Assisted Methods Lung Neoplasms Diagnosis Biopsy Methods Human Cancer Screening Methods Sensitivity and Specificity ab: This study explores an automatic diagnosis method to predict unnecessary nodule biopsy from a small, unbalanced, and pathologically proven database. The automatic diagnosis method is based on a convolutional neural network (CNN) model. Because of the small and unbalanced samples, the presented method aims to improve the transfer learning capability via the VGG16 architecture and optimize the related transfer learning parameters. For comparison purpose, a traditional machine learning method is implemented, which extracts the texture features and classifies the features by support vector machine (SVM). The database includes 68 biopsied nodules, 16 are pathologically proven benign and the remaining 52 are malignant. To consider the volumetric data by the CNN model, each image slice from each nodule volume is selected randomly until all image slices of each nodule are utilized. The leave-one-out and 10-folder cross validations are applied to train and test the randomly selected 68 image slices (one image slice from one nodule) in each experiment, respectively. The averages over all the experimental outcomes are the final results. The experiments revealed that the features from both the medical and the natural images share the similarity of focusing on simpler and less-abstract objects, leading to the conclusion that not the more the transfer convolutional layers, the better the classification results. Transfer learning from other larger datasets can supply additional information to small and unbalanced datasets to improve the classification performance. The presented method has shown the potential to adapt CNN architecture to improve the prediction of unnecessary nodule biopsy from small, unbalanced, and pathologically proven volumetric dataset. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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