# MRNet: Multifaceted Resilient Networks for Medical Image-to-Image Translation

arXiv: https://arxiv.org/abs/2412.03039 · HTML: https://mlsanity.com/paper/2412.03039/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2024.

## Abstract (arXiv)

We propose a Multifaceted Resilient Network(MRNet), a novel architecture developed for medical image-to-image translation that outperforms state-of-the-art methods in MRI-to-CT and MRI-to-MRI conversion. MRNet leverages the Segment Anything Model (SAM) to exploit frequency-based features to build a powerful method for advanced medical image transformation. The architecture extracts comprehensive multiscale features from diverse datasets using a powerful SAM image encoder and performs resolution-aware feature fusion that consistently integrates U-Net encoder outputs with SAM-derived features. This fusion optimizes the traditional U-Net skip connection while leveraging transformer-based contextual analysis. The translation is complemented by an innovative dual-mask configuration incorporating dynamic attention patterns and a specialized loss function designed to address regional mapping mismatches, preserving both the gross anatomy and tissue details. Extensive validation studies have shown that MRNet outperforms state-of-the-art architectures, particularly in maintaining anatomical fidelity and minimizing translation artifacts.