User Loss -- A Forced-Choice-Inspired Approach to Train Neural Networks directly by User Interaction
This enables personalized deep learning models for applications like medical imaging where user preferences vary, though it is incremental as it builds on existing forced-choice and precision learning methods.
The paper tackles the problem of training neural networks directly from user inputs, specifically for medical image denoising where optimality is user-dependent, and demonstrates that models can be tailored to individual experts with different filter preferences, achieving best performance on each user's test data.
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.