Modeling the Biological Pathology Continuum with HSIC-regularized Wasserstein Auto-encoders

01/20/2019
by   Denny Wu, et al.
0

A crucial challenge in image-based modeling of biomedical data is to identify trends and features that separate normality and pathology. In many cases, the morphology of the imaged object exhibits continuous change as it deviates from normality, and thus a generative model can be trained to model this morphological continuum. Moreover, given side information that correlates to certain trend in morphological change, a latent variable model can be regularized such that its latent representation reflects this side information. In this work, we use the Wasserstein Auto-encoder to model this pathology continuum, and apply the Hilbert-Schmitt Independence Criterion (HSIC) to enforce dependency between certain latent features and the provided side information. We experimentally show that the model can provide disentangled and interpretable latent representations and also generate a continuum of morphological changes that corresponds to change in the side information.

READ FULL TEXT

page 4

page 8

research
11/05/2017

Wasserstein Auto-Encoders

We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for buil...
research
05/22/2018

Information Constraints on Auto-Encoding Variational Bayes

Parameterizing the approximate posterior of a generative model with neur...
research
03/20/2019

Part-based approximations for morphological operators using asymmetric auto-encoders

This paper addresses the issue of building a part-based representation o...
research
02/18/2020

SentenceMIM: A Latent Variable Language Model

We introduce sentenceMIM, a probabilistic auto-encoder for language mode...
research
04/24/2023

Variational Diffusion Auto-encoder: Deep Latent Variable Model with Unconditional Diffusion Prior

Variational auto-encoders (VAEs) are one of the most popular approaches ...
research
06/22/2020

Modeling Lost Information in Lossy Image Compression

Lossy image compression is one of the most commonly used operators for d...
research
06/10/2021

Dynamic Shape Modeling to Analyze Modes ofMigration During Cell Motility

This paper develops a generative statistical model for representing, mod...

Please sign up or login with your details

Forgot password? Click here to reset