Effect of Training Data Volume on Performance of Convolutional Neural Network Pneumothorax Classifiers.
Large datasets with high-quality labels required to train deep neural networks are challenging to obtain in the radiology domain. This work investigates the effect of training dataset size on the performance of deep learning classifiers, focusing on chest radiograph pneumothorax detection as a proxy...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 4; pp. 881 - 893 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2022
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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=159195613&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159195613 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2022 vid: 35 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159195613 155542238 159195613 159195613 10.1007/s10278-022-00594-y 159195613 ppf: 881 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Effect of Training Data Volume on Performance of Convolutional Neural Network Pneumothorax Classifiers. aug: au: Thian, Yee Liang Ng, Dian Wen Hallinan, James Thomas Patrick Decourcy Jagmohan, Pooja Sia, Soon Yiew Mohamed, Jalila Sayed Adnan Quek, Swee Tian Feng, Mengling affil: Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, 119074, Queenstown, Singapore sug: subj: Pneumothorax Neural Networks (Computer) Deep Learning Digital Imaging Image Processing, Computer Assisted Methods Human Descriptive Statistics Data Analysis Software Learning ab: Large datasets with high-quality labels required to train deep neural networks are challenging to obtain in the radiology domain. This work investigates the effect of training dataset size on the performance of deep learning classifiers, focusing on chest radiograph pneumothorax detection as a proxy visual task in the radiology domain. Two open-source datasets (ChestX-ray14 and CheXpert) comprising 291,454 images were merged and convolutional neural networks trained with stepwise increase in training dataset sizes. Model iterations at each dataset volume were evaluated on an external test set of 525 emergency department chest radiographs. Learning curve analysis was performed to fit the observed AUCs for all models generated. For all three network architectures tested, model AUCs and accuracy increased rapidly from 2 × 103 to 20 × 103 training samples, with more gradual increase until the maximum training dataset size of 291 × 103 images. AUCs for models trained with the maximum tested dataset size of 291 × 103 images were significantly higher than models trained with 20 × 103 images: ResNet-50: AUC20k = 0.86, AUC291k = 0.95, p < 0.001; DenseNet-121 AUC20k = 0.85, AUC291k = 0.93, p < 0.001; EfficientNet AUC20k = 0.92, AUC 291 k = 0.98, p < 0.001. Our study established learning curves describing the relationship between dataset training size and model performance of deep learning convolutional neural networks applied to a typical radiology binary classification task. These curves suggest a point of diminishing performance returns for increasing training data volumes, which algorithm developers should consider given the high costs of obtaining and labelling radiology data. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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