The paper proposes a unified, contrast-agnostic framework that uses parameter-informed disentanglement and adaptive experts to robustly correct motion artifacts in MRI across various modalities and se…

The paper proposes In-Context Visual Contrastive Optimization (IC-VCO) to rigorously mitigate multimodal hallucinations in Vision-Language Models by optimizing contrastive learning within a shared mul…

The paper proposes RAMP, a multi-corruption augmentation framework, which significantly improves the robustness and reliability of CT segmentation deep learning models when deployed in real-world, deg…

The paper introduces a histogram-regularized latent diffusion model to synthesize highly realistic and subtype-specific pulmonary nodules in 3D CT volumes, addressing the limitations of existing metho…

This paper proposes a plug-and-play gradient-step model that effectively reduces photon noise in dental cone-beam CT reconstruction by incorporating a data-driven denoiser prior.

The paper proposes a Ferroelectric Charge-Domain Compute Cell (FCDC) using HZO memcapacitors to perform attention computation, achieving significant energy efficiency gains, especially for long-reside…

This paper introduces MedReCo and MedReCo-VLM, a framework that enables entity-aware cross-image reasoning for medical imaging, allowing AI to compare current scans with prior studies and analogous ca…

The paper proposes using a mask-conditioned latent diffusion model to generate synthetic, labeled TEM images for data augmentation, achieving small but measurable performance improvements in defect de…

The paper proposes a novel Generative Counterfactual Attention-guided Network (GCAN) that uses multimodal connectomes and brain atlas knowledge to provide explainable and highly accurate diagnosis of…

The paper introduces CardioLens, a rigorous evaluation testbed for multi-sequence Cardiac MRI, which reveals that current Multimodal Large Language Models (MLLMs) exhibit a significant 'clinical reali…

The paper addresses 'Template Collapse' in 3D CT report generation—where models generate generic reports—by proposing CLarGen, a decoupled framework that significantly improves clinical accuracy and d…

The paper proposes VDSB-GWSyn, a Diffusion Schrödinger Bridge framework, to synthesize controllable and anatomically feasible guidewire images on coronary angiography (CAG) scans, significantly improv…

The paper introduces PIGMENT, a physics-informed foundation model that enables reliable quantitative mapping of brain microstructure from extremely sparse or challenging diffusion MRI scans.

The paper introduces Dual-Spectral Flow Matching (DSFM), a novel generative framework that uses wavelet and cosine transforms to synthesize highly realistic, non-stationary fMRI time series for improv…

The paper proposes a measurement-geometry framework to quantify how well fixed measurement operators can distinguish between images generated by a prior, thereby guiding the design of more trustworthy…

The paper systematically characterizes column-level activation sparsity across various diffusion model architectures, demonstrating that element-level sparsity metrics significantly overestimate the a…

This paper identifies prediction bias, a failure mode of entropy minimization in test-time adaptation, and proposes Distribution Shift Bias Reduction (DSBR) to stabilize adaptation and prevent model c…