# pyMEAL: A Multi-Encoder Augmentation-Aware-Learning Toolbox for Robust Medical Image Translation

arXiv: https://arxiv.org/abs/2505.24421 · HTML: https://mlsanity.com/paper/2505.24421/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 64 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 10 of 10)

- [What Should Not Be Contrastive in Contrastive Learning](https://mlsanity.com/paper/2008.05659/) (2020)
- [Can contrastive learning avoid shortcut solutions?](https://mlsanity.com/paper/2106.11230/) (2021)
- [Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review](https://mlsanity.com/paper/2307.13125/) (2023)
- [Tackling Structural Hallucination in Image Translation with Local Diffusion](https://mlsanity.com/paper/2404.05980/) (2024)
- [Slice-Consistent 3D Volumetric Brain CT-to-MRI Translation with 2D Brownian Bridge Diffusion Model](https://mlsanity.com/paper/2407.05059/) (2024)
- [A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks](https://mlsanity.com/paper/2206.06544/) (2022)
- [Tackling Bias in the Dice Similarity Coefficient: Introducing nDSC for White Matter Lesion Segmentation](https://mlsanity.com/paper/2302.05432/) (2023)
- [Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis](https://mlsanity.com/paper/2303.01178/) (2023)
- [Augmentation-aware Self-supervised Learning with Conditioned Projector](https://mlsanity.com/paper/2306.06082/) (2023)
- [AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification](https://mlsanity.com/paper/2503.21105/) (2025)

## Abstract (arXiv)

Medical imaging plays a vital role in clinical diagnosis, yet AI-driven imaging methods remain challenged by patient variability, image artifacts, and limited robustness across acquisition conditions. Although deep learning has advanced medical image analysis, 3D image translation remains hindered by limited training data and variability arising from scanner differences, imaging protocols, and patient motion. Conventional data augmentation typically relies on a single transformation pipeline, overlooking augmentation-specific characteristics and limiting representation learning. To address these challenges, we propose Multi-Encoder Augmentation-Aware Learning (MEAL), which processes multiple augmentation variants through dedicated encoder pathways. Three feature integration strategies are investigated: encoder concatenation (MEAL-CC), fusion layer (MEAL-FL), and an adaptive controller block (MEAL-BD). By dynamically weighting augmentation-specific features before decoding, MEAL-BD preserves complementary representations and improves robustness to clinically relevant variability. We evaluate MEAL using CT-to-T1-weighted MRI translation, a clinically relevant task when MRI is unavailable, contraindicated, or delayed. Across predefined and unseen test datasets, MEAL-BD consistently outperformed competing approaches under both geometric perturbations and standard imaging conditions, achieving higher peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). By prioritizing structural fidelity over perceptual realism, MEAL supports clinical interpretation and downstream image analysis rather than replacing diagnostic MRI, demonstrating that augmentation-aware representation learning improves the robustness and clinical applicability of medical image translation.