Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases

TL;DR

This paper introduces Subspace Network, a deep multi-task censored regression model that captures non-linear interactions and respects clinical score bounds, improving neurodegenerative disease prediction from neuroimaging data.

Contribution

It presents a novel deep learning approach that explicitly models bounds and non-linearities in multi-task censored regression for neurodegenerative disease analysis.

Findings

Outperforms existing methods in predicting clinical scores

Efficiently recovers parameter subspaces with minimal data passes

Demonstrates rapid learning of correct parameter subspaces

Abstract

Over the past decade a wide spectrum of machine learning models have been developed to model the neurodegenerative diseases, associating biomarkers, especially non-intrusive neuroimaging markers, with key clinical scores measuring the cognitive status of patients. Multi-task learning (MTL) has been commonly utilized by these studies to address high dimensionality and small cohort size challenges. However, most existing MTL approaches are based on linear models and suffer from two major limitations: 1) they cannot explicitly consider upper/lower bounds in these clinical scores; 2) they lack the capability to capture complicated non-linear interactions among the variables. In this paper, we propose Subspace Network, an efficient deep modeling approach for non-linear multi-task censored regression. Each layer of the subspace network performs a multi-task censored regression to improve upon the predictions from the last layer via sketching a low-dimensional subspace to perform knowledge transfer among learning tasks. Under mild assumptions, for each layer the parametric subspace can be recovered using only one pass of training data. Empirical results demonstrate that the proposed subspace network quickly picks up the correct parameter subspaces, and outperforms state-of-the-arts in predicting neurodegenerative clinical scores using information in brain imaging.