The paper introduces FAN-LoRA, a Fourier‑adaptive nonlinear low‑rank adaptor designed to improve domain adaptation of vision foundation models like SAM for medical imaging. By decoupling frequency components into a low‑pass B‑spline branch for global structure and a high‑pass Fourier branch for local texture, FAN‑LoRA addresses performance drops caused by domain shifts. Experiments on three cross‑modality and cross‑center benchmarks show that FAN‑LoRA outperforms existing PEFT methods, achieving higher Dice scores and lower boundary errors while remaining computationally efficient.
arXiv:2609.13654v1 Announce Type: new Abstract: Pretrained foundation models (FMs) have achieved remarkable success in computer vision, yet their high fine-tuning cost limits practical deployment. Pa...
arXiv:2608.21300v1 Announce Type: new Abstract: Foundation models for medical image segmentation, like prompt-based MedSAM, generalize well across domains and modalities, often in zero or few-shot se...
arXiv:2610.35220v1 Announce Type: new Abstract: Medical image segmentation (MIS) remains challenging due to composite degradations and multi-scale geometric variations, which jointly impair lesion-ba...
arXiv:2608. 07749v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning enables the adaptation of vision foundation models to biomedical tasks under limited computational resources, but a single low-rank update can constrain all task-specific changes to one narrow parameter subspace.
arXiv:2505. 12532v3 Announce Type: replace-cross Abstract: Efficiently adapting large pretrained models is critical under tight compute and memory budgets.
arXiv:2610.08109v1 Announce Type: new Abstract: Cardiac magnetic resonance imaging reconstruction aims to recover high-quality images from undersampled acquisitions, enabling faster scans while prese...
DGCM-SSFA is a compact multi‑domain attention framework that synthesizes high‑fidelity 7T MRI from routine 3T scans using only 11 million parameters. It combines a Dual‑Granularity Cross‑Modulated Feature Modulator (DGCM) and a Sparse Strip Frequency Attention (SSFA) module within a U‑Net Transformer backbone to enhance local edge details and capture global spatial context efficiently. Experiments on UNC and BNU 3T‑to‑7T datasets show competitive structural fidelity, achieving SSIM scores of 0.8431 (BNU T2w) and 0.8100 (BNU T1w) while remaining far smaller than Transformer‑based alternatives.
arXiv:2506.24092v3 Announce Type: replace Abstract: Adapting large pretrained vision models to medical image classification is often limited by memory, computation, and task-specific specializations....
arXiv:2610.27656v1 Announce Type: new Abstract: Medical visual grounding requires models to align clinical descriptions with spatial evidence across heterogeneous imaging domains. However, existing m...
arXiv:2610.33402v1 Announce Type: new Abstract: Existing Ultra-High-Definition (UHD) low-light image enhancement (LLIE) methods typically rely on either spatial- or frequency-domain modeling, or inde...
arXiv:2607. 06918v1 Announce Type: cross Abstract: Pre-trained Vision Foundation Models (VFMs) provide strong visual representations for diverse downstream tasks.
arXiv:2608. 11335v1 Announce Type: cross Abstract: Clinical text can narrow down what to segment, but recent text-guided designs emphasize spatial alignment while overlooking frequency content that governs texture and boundaries.