Latest AI and machine learning research in work force for healthcare professionals.
FeePredict is a three-stage random forest machine learning framework to simul-taneously predict whether Medicare reimbursement rates for specific procedures will change, in which direction they will change, and by how much. FeePredict was ap-plied to the four major Medicare fee schedules: the Clinical Laboratory Fee Schedule (CLFS), the Physician Fee Schedule (PFS), the Ambulance Fee Schedule (AFS...
Few-step distilled diffusion models generate high-quality images quickly, but often lose per-prompt diversity, producing near-identical samples across random seeds. Optimizing the initial noise at inference time offers an appealing way to recover this diversity, yet existing methods directly update the initial noise in an unconstrained Euclidean space, ignoring both the geometry of the Gaussian pr...
Current identity customized video generation methodologies are predominantly limited to single-identity scenarios, as the lack of explicit identity se...
The genus Pseudomonas consists of diverse and ecologically significant species that form close associations with both plants and animals. This genus i...
Key regulators of neural network activity in multiple advanced cognitive processes and essential components of the blood-brain barrier, astrocytes con...
Physical simulations that predict the behavior of urban disasters, such as climate-related flooding, play a crucial role in disaster prevention and th...
Abstract Background: Remote clinical reviews have become an integral component of contemporary nursing practice across community and acute care settin...
Industrial anomaly detection aims to identify and localize defective regions without relying on exhaustive annotations of all possible defect types. A...
Recent visual place recognition (VPR) studies have increasingly relied on large-scale datasets to train more robust and discriminative models. Althoug...
Text-to-image (T2I) models can synthesize realistic, prompt-aligned images, yet samples generated for the same prompt often cover only a small subset ...
Recently, the societal implementation of high-performance image classification models has expanded rapidly. While these models require vast amounts of...
Emergency brain computed tomography (CT) is the first line imaging modality for patients with acute neurological symptoms and trauma, where delayed or...
While diffusion models achieve state-of-the-art image quality for text-to-image (T2I) generation, recent work has demonstrated that they suffer from s...
Explainable machine learning (XML) pipelines applied to composite mental health outcomes can produce apparently-robust, cross-population-stable risk h...
Chest radiography (CXR) remains the most widely used thoracic imaging modality, yet expert interpretation is constrained by a severe shortage of radio...
Many insects manipulate plants by injecting effector proteins. In one extreme example of this molecular "hijacking", Hormaphis cornu aphids inject bic...
Large vision-language models incur substantial inference costs because high-resolution inputs introduce thousands of visual tokens, many of which are ...
Subject-driven personalized text-to-image generation requires a pretrained diffusion model to acquire a specific subject from a few reference images w...
Interactive simulators have become powerful tools for training embodied agents and generating synthetic visual data, but existing photorealistic simul...
Existing NAS benchmarks (e.g., NAS-Bench, NATS-Bench) cover only narrow, task-specific regions of the architectural design space and lack cross-domain...