Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
Super-resolution is widely used in medical imaging to enhance low-quality data, reducing scan time and improving abnormality detection. Conventional super-resolution approaches typically rely on paired datasets of downsampled and original high resolution images, training models to reconstruct high resolution images from their artificially degraded counterparts. However, in real-world clinical sett...
Personalized text-to-image generation aims to integrate specific identities into arbitrary contexts. However, existing tuning-free methods typically employ Spatially Uniform Visual Injection, causing identity features to contaminate non-facial regions (e.g., backgrounds and lighting) and degrading text adherence. To address this without expensive fine-tuning, we propose SpatialID, a training-free ...
Diffusion-based image generators are promising priors for ill-posed inverse problems like sparse-view X-ray Computed Tomography (CT). As most studies ...
Efficient coding is essential for sensory systems to extract meaningful information from the environment. Here, we investigate how stimulus-driven the...
Language processing is supported by distributed neural systems. Yet most research examines these systems at the population-average level, obscuring ho...
In machine learning, "ground truth" refers to the assumed correct labels used to train and evaluate models. However, the foundational "ground truth" p...
Object-level manipulation, relocating or reorienting objects in images or videos while preserving scene realism, is central to film post-production, A...
Speech is a defining human behavior, and this ability depends critically on speech motor cortex. While the ventral precentral and postcentral gyri are...
Modern segmentation models achieve strong predictive performance but remain largely opaque, limiting our ability to diagnose failures, understand data...
Large-scale diffusion models such as FLUX (12B parameters) and Stable Diffusion 3 (8B parameters) require multi-GPU parallelism for efficient inferenc...
Synthetic data provide low-cost, accurately annotated samples for geometry-sensitive vision tasks, but appearance and imaging differences between synt...
Synthetic data provide low-cost, accurately annotated samples for geometry-sensitive vision tasks, but appearance and imaging differences between synt...
Fine-tuning large language models (LLMs) for specialized domains often necessitates a trade-off between acquiring domain expertise and retaining gener...
Objective To characterize the clinical and administrative concerns communicated through secure ophthalmology messaging and to assess differences in me...
Robust generalization under distribution shift remains difficult to monitor and optimize in the absence of target-domain labels, as models with simila...
Unsupervised anomaly detection stands as an important problem in machine learning, with applications in financial fraud prevention, network security a...
Masked Autoencoders (MAEs) achieve impressive performance in image classification tasks, yet the internal representations they learn remain less under...
Introduction: In neurosurgery, image-guided Neurosurgery Systems (IGNS) highly rely on preoperative brain magnetic resonance images (MRI) to assist su...
Despite being trained on balanced datasets, existing AI-generated image detectors often exhibit systematic bias at test time, frequently misclassifyin...
Continual learning aims to acquire tasks sequentially without catastrophic forgetting, yet standard strategies face a core tradeoff: regularization-ba...