# Multi-Stage Prediction Networks for Data Harmonization

## TL;DR

This paper presents a multi-task learning framework called Multi Stage Prediction Network for data harmonization across different imaging platforms, significantly improving prediction accuracy and efficiency in medical image harmonization tasks.

## Contribution

The paper introduces the MSP network, a novel multi-task learning architecture that integrates disparate neural networks for improved data harmonization performance.

## Key findings

- MSP achieves around 20% improvement in mean-squared error over state-of-the-art methods.
- MSP outperforms standard off-the-shelf multi-task learning networks.
- Validated on a dMRI harmonization dataset with multiple platform types.

## Abstract

In this paper, we introduce multi-task learning (MTL) to data harmonization (DH); where we aim to harmonize images across different acquisition platforms and sites. This allows us to integrate information from multiple acquisitions and improve the predictive performance and learning efficiency of the harmonization model. Specifically, we introduce the Multi Stage Prediction (MSP) Network, a MTL framework that incorporates neural networks of potentially disparate architectures, trained for different individual acquisition platforms, into a larger architecture that is refined in unison. The MSP utilizes high-level features of single networks for individual tasks, as inputs of additional neural networks to inform the final prediction, therefore exploiting redundancy across tasks to make the most of limited training data. We validate our methods on a dMRI harmonization challenge dataset, where we predict three modern platform types, from one obtained from an old scanner. We show how MTL architectures, such as the MSP, produce around 20\% improvement of patch-based mean-squared error over current state-of-the-art methods and that our MSP outperforms off-the-shelf MTL networks. Our code is available https://github.com/sbb-gh/ .