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Proceedings Paper

Deep variational auto-encoders for unsupervised glomerular classification
Author(s): Brendon Lutnick; Rabi Yacoub; Kuang-Yu Jen; John E. Tomaszewski; Sanjay Jain; Pinaki Sarder
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Paper 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.

Paper Details

Date Published: 6 March 2018
PDF: 7 pages
Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105810C (6 March 2018); doi: 10.1117/12.2295456
Show Author Affiliations
Brendon Lutnick, Univ. at Buffalo (United States)
Rabi Yacoub, Univ. at Buffalo (United States)
Kuang-Yu Jen, Univ. of California, Davis (United States)
John E. Tomaszewski, Univ. at Buffalo (United States)
Sanjay Jain, Washington Univ. School of Medicine in St. Louis (United States)
Pinaki Sarder, Univ. at Buffalo (United States)


Published in SPIE Proceedings Vol. 10581:
Medical Imaging 2018: Digital Pathology
John E. Tomaszewski; Metin N. Gurcan, Editor(s)

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