Slice-selective learning for Alzheimer's disease classification using a generative adversarial network: a feasibility study of external validation.
Purpose: The aim of this feasibility study was to use slice selective learning using a Generative Adversarial Network for external validation. We aimed to build a model less sensitive to PET imaging acquisition environment, since differences in environments negatively influence network performance....
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 47; no. 9; pp. 2197 - 2207 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2020
|
| 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=144404628&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144404628 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Aug2020 vid: 47 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 144404628 144121612 10.1007/s00259-019-04676-y 144404628 ppf: 2197 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Slice-selective learning for Alzheimer's disease classification using a generative adversarial network: a feasibility study of external validation. aug: au: Kim, Han Woong Lee, Ha Eun Lee, Sangwon Oh, Kyeong Taek Yun, Mijin Yoo, Sun Kook affil: Department of Medical Engineering, Yonsei University College of Medicine, Seoul, Republic of Korea sug: ab: Purpose: The aim of this feasibility study was to use slice selective learning using a Generative Adversarial Network for external validation. We aimed to build a model less sensitive to PET imaging acquisition environment, since differences in environments negatively influence network performance. To investigate the slice performance, each slice evaluation was performed. Methods: We trained our model using a 18F-fluorodeoxyglucose ([18F]FDG) PET/CT dataset obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and tested the model with a Severance Hospital dataset. We applied slice selective learning to reduce computational cost and to extract unbiased features. We extracted features of Alzheimer's disease (AD) and normal cognitive (NC) condition using a Boundary Equilibrium Generative Adversarial Network (BEGAN) for stable convergence. Then, we utilized these features to train a support vector machine (SVM) classifier to distinguish AD from NC. Results: The slice range that covered the posterior cingulate cortex (PCC) using double slices showed the best performance. The accuracy, sensitivity, and specificity of our proposed network was 94.33%, 91.78%, and 97.06% using the Severance dataset and 94.82%, 92.11%, and 97.45% using the ADNI dataset. The performance on the two independent datasets showed no statistical difference (p > 0.05). Moreover, there was a statistical difference in the performance between using two slices and one slice as input (p < 0.05). Conclusions: Our model learned the generalized features of AD and NC for external validation when appropriate slices were selected. This study showed the feasibility of this model with consistent performance when tested using datasets acquired from a variety of image-acquisition environments. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|