User Loss -- A Forced-Choice-Inspired Approach to Train Neural Networks directly by User Interaction

TL;DR

This paper introduces a novel method to train neural networks directly from user feedback, especially in medical imaging, by using a forced-choice-inspired loss function and a minimal-parameter architecture, enabling personalized image enhancement.

Contribution

It proposes a new approach to incorporate user interaction into neural network training through a forced-choice-based loss function and a precision learning architecture with few parameters.

Findings

Models can be tailored to individual user preferences.

Different expert preferences can be captured with the approach.

User-specific models outperform generic models on individual test data.

Abstract

In this paper, we investigate whether is it possible to train a neural network directly from user inputs. We consider this approach to be highly relevant for applications in which the point of optimality is not well-defined and user-dependent. Our application is medical image denoising which is essential in fluoroscopy imaging. In this field every user, i.e. physician, has a different flavor and image quality needs to be tailored towards each individual. To address this important problem, we propose to construct a loss function derived from a forced-choice experiment. In order to make the learning problem feasible, we operate in the domain of precision learning, i.e., we inspire the network architecture by traditional signal processing methods in order to reduce the number of trainable parameters. The algorithm that was used for this is a Laplacian pyramid with only six trainable parameters. In the experimental results, we demonstrate that two image experts who prefer different filter characteristics between sharpness and de-noising can be created using our approach. Also models trained for a specific user perform best on this users test data. This approach opens the way towards implementation of direct user feedback in deep learning and is applicable for a wide range of application.