Task-aware denoising autoencoders for establishing efficient channels

Abstract

It has been suggested that medical imaging systems should be evaluated and optimized by use of task-based measures of image quality (IQ). Task-based measures of IQ summarize the performance of an observer at a specific task (e.g., tumor detection). The Hotelling observer (HO) is a commonly employed numerical observer for evaluating and optimizing medical imaging systems. However, the computation of the HO can be intractable when huge covariance matrices of the image data need to be inverted. One way to address this issue is to apply a set of channels to the image data and subsequently compute the HO on the channelized data. When the channels are efficient, the HO performance can be approximated by the performance of the channelized Hotelling observer (CHO). However, it remains unclear how efficient channels can be learned and subsequently employed when performing image processing tasks. In this work, I propose a task-aware method for training denoising autoencoders (DAEs) for establishing efficient channels that can be employed for image denoising. It is demonstrated that the HO performance can be closely approximated by use of the proposed task-aware DAE-learned channels. In addition, the images produced by the proposed task-aware DAEs can achieve improved signal detectability evaluated by a foveated CHO, which was developed for modeling human visual systems.