Deep variational auto-encoders for unsupervised glomerular classification

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

doi:10.1117/12.2295456

6 March 2018 Deep variational auto-encoders for unsupervised glomerular classification

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

Proceedings Volume 10581, Medical Imaging 2018: Digital Pathology; 105810C (2018) https://doi.org/10.1117/12.2295456
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Brendon Lutnick, Rabi Yacoub, Kuang-Yu Jen, John E. Tomaszewski, Sanjay Jain, and Pinaki Sarder "Deep variational auto-encoders for unsupervised glomerular classification", Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105810C (6 March 2018); https://doi.org/10.1117/12.2295456

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