MRNet: Multifaceted Resilient Networks for Medical Image-to-Image Translation
This addresses medical imaging challenges for healthcare applications, but it appears incremental as it builds on existing models like U-Net and SAM.
The paper tackles medical image-to-image translation, proposing MRNet, which outperforms state-of-the-art methods in MRI-to-CT and MRI-to-MRI conversion by maintaining anatomical fidelity and minimizing artifacts.
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.