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Generative modeling for label-free glomerular modeling and classification
- Source :
- Medical Imaging: Digital Pathology, Proc SPIE Int Soc Opt Eng
- Publication Year :
- 2020
- Publisher :
- SPIE, 2020.
-
Abstract
- Generative modeling using GANs has gained traction in machine learning literature, as training does not require labeled datasets. This is perfect for applications in biological datasets, where large labeled datasets are often difficult and expensive to acquire. However, generative models offer no easy way to encode real images into feature-sets, something that is desirable for network explainability and may yield potentially informative image features. For this reason, we test a VAE-GAN architecture for label-free modeling of glomerular structural features. We show that this network can generate realistic looking synthetic images, and be used to interpolate between images. To prove the biological relevance of the network encodings, we classify small-labeled sets of encoded glomeruli by biopsy Tervaert class and for the presence of sclerosis, obtaining a Cohen’s kappa values of 0.87 and 0.78 respectfully.
- Subjects :
- business.industry
Computer science
generative adversarial network
Bioengineering
Pattern recognition
glomeruli
Real image
ENCODE
Class (biology)
Article
Unsupervised data-mining
Generative modeling
variational autoencoder
Relevance (information retrieval)
Artificial intelligence
business
Generative adversarial network
Generative grammar
Label free
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Medical Imaging 2020: Digital Pathology
- Accession number :
- edsair.doi.dedup.....1aa9647cbc828dc84313b1c817d493f3