Generative modeling for label-free glomerular modeling and classification

Brendon Lutnick; Brandon Ginley; Kuang-Yu Jen; Wen Dong; Pinaki Sarder

doi:10.1117/12.2548757

16 March 2020 Generative modeling for label-free glomerular modeling and classification

Brendon Lutnick, Brandon Ginley, Kuang-Yu Jen, Wen Dong, Pinaki Sarder

Proceedings Volume 11320, Medical Imaging 2020: Digital Pathology; 1132007 (2020) https://doi.org/10.1117/12.2548757
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Conference Presentation

Citation Download Citation

Brendon Lutnick, Brandon Ginley, Kuang-Yu Jen, Wen Dong, and Pinaki Sarder "Generative modeling for label-free glomerular modeling and classification", Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 1132007 (16 March 2020); https://doi.org/10.1117/12.2548757

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