Deep variational auto-encoders for unsupervised glomerular classification

Brendon Lutnick, Rabi Yacoub, Kuang Yu Jen, John E. Tomaszewski, Sanjay Jain, Pinaki Sarder

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The adoption of deep learning techniques in medical applications has thus far been limited by the availability of the large labeled datasets required to robustly train neural networks, as well as difficulty interpreting these networks. However, recent techniques for unsupervised training of neural networks promise to address these issues, leveraging only structure to model input data. We propose the use of a variational autoencoder (VAE) which utilizes data from an animal model to augment the training set and non-linear dimensionality reduction to map this data to human sets. This architecture utilizes variational inference, performed on latent parameters, to statistically model the probability distribution of training data in a latent feature space. We show the feasibility of VAEs, using images of mouse and human renal glomeruli from various pathological stages of diabetic nephropathy (DN), to model the progression of structural changes which occur in DN. When plotted in a 2-dimentional latent space, human and mouse glomeruli, show separation with some overlap, suggesting that the data is continuous, and can be statistically correlated. When DN stage is plotted in this latent space, trends in disease pathology are visualized.

Original languageEnglish
Title of host publicationMedical Imaging 2018
Subtitle of host publicationDigital Pathology
EditorsMetin N. Gurcan, John E. Tomaszewski
ISBN (Electronic)9781510616516
StatePublished - 2018
EventMedical Imaging 2018: Digital Pathology - Houston, United States
Duration: Feb 11 2018Feb 12 2018

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2018: Digital Pathology
Country/TerritoryUnited States


  • Cross species modeling
  • Knowledge transfer
  • Unsupervised modeling
  • Variational autoencoder


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