An evolvable adversarial network with gradient penalty for COVID-19 infection segmentation
| Autor: | Kai Zhang, Piaoyao Yu, He Juanjuan, Qi Zhu, Jinshan Tang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
education.field_of_study
Discriminator Computer science Population Stability (learning theory) Evolutionary algorithm COVID-19 Wasserstein generative adversarial network Lipschitz continuity Article Infection segmentation Norm (mathematics) Gradient penalty Segmentation education Algorithm Software Generator (mathematics) |
| Zdroj: | Applied Soft Computing |
| ISSN: | 1568-4946 |
| DOI: | 10.1016/j.asoc.2021.107947 |
| Popis: | COVID-19 infection segmentation has essential applications in determining the severity of a COVID-19 patient and can provide a necessary basis for doctors to adopt a treatment scheme. However, in clinical applications, infection segmentation is performed by human beings, which is time-consuming and generally introduces bias. In this paper, we developed a novel evolvable adversarial framework for COVID-19 infection segmentation. Three generator networks compose an evolutionary population to accommodate the current discriminator, i.e., generator networks evolved with different mutations instead of the single adversarial objective to provide sufficient gradient feedback. Compared with the existing work that enforces a Lipschitz constraint by weight clipping, which may lead to gradient exploding or vanishing, the proposed model also incorporates the gradient penalty into the network, penalizing the discriminator's gradient norm input. Experiments on several COVID-19 CT scan datasets verified that the proposed method achieved superior effectiveness and stability for COVID-19 infection segmentation. |
| Databáze: | OpenAIRE |
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