Latest AI and machine learning research in work force for healthcare professionals.
A global shortage of trained sonographers limits prenatal ultrasound screening in low- and middle-in...
Recent text-to-image models built on large-scale Transformer backbones and flow-based objectives del...
In real-world applications, models are expected to perform reliably across diverse settings. Yet, ma...
Robust training and validation of Autonomous Driving Systems (ADS) require massive, diverse datasets...
Deep neural networks have achieved impressive performance across a wide range of tasks, but this suc...
Background: Large language models (LLMs) are increasingly used in telehealth, but their safety in an...
Distilled one-step (T=1) or few-step (T$\leq$4) diffusion models enable real-time image generation b...
RLHF is widely used to align flow-matching text-to-image models with human preferences, but often le...
We present EDDY (Exact-marginal Diversification via Divergence-free dYnamics), a guidance mechanism ...
Background: Emerging evidence suggests that the oral microbiome may contribute to aberrant gut immun...
Background: Diabetic retinopathy (DR) is the leading cause of preventable blindness among working-ag...
Knowledge distillation (KD) is a well-known technique to effectively compress a large network (teach...
Training reliable respiratory sound classification models remains challenging due to the limited siz...
Plankton monitoring is essential for assessing aquatic ecosystems but is limited by the labor-intens...
Large-scale dataset distillation requires storing auxiliary soft labels that can be 30-40x larger on...
Composed Image Retrieval (CIR) aims to retrieve target images by integrating a reference image with ...
Background: Datasets related to infectious diseases are essential for public health decision-making,...
We address the problem of tactile localization, where the goal is to identify image regions that sha...
While the shortage of explicit action data limits Vision-Language-Action (VLA) models, human action ...
Deep learning underpins a wide range of applications in MRI, including reconstruction, artifact remo...
Cell state diversity drives tissue adaptability, repair, and disease resilience, but fully capturing...