Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
Background: Previous machine learning models to intraoperatively predict the molecular status of gliomas using stimulated Raman histology (SRH), such as DeepGlioma, have achieved high performance (91.5% accuracy) on curated datasets. However, when used intraoperatively, DeepGlioma (162M parameters) runs slowly on current SRH hardware and underperforms due to its lack of an image rejection mechanis...
The Post-baccalaureate Research Education Program (PREP), established by the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health in 2000, was a research-intense, one-year training program for recent college graduates from backgrounds uncommon in science who intended to matriculate with a PhD or MD/PhD program in preparation for an eventual career in biomedic...
The accurate recovery of constituent-level optical properties from integrating sphere measurements is a central analytical challenge in pharmaceutical...
The inherent electronic and speckle noise complicates clinical interpretation of ultrasound images. Conventional denoising methods rely on explicit no...
Differentiable vector graphics have enabled powerful gradient-based optimization of vector primitives directly from raster images. However, existing f...
Diffusion models (DMs) have demonstrated remarkable success in real-world image super-resolution (SR), yet their reliance on time-consuming multi-step...
Deep learning-based facial phenotyping represents a major paradigm shift in the diagnosis of rare and ultra-rare genetic disorders. By capturing disea...
ABSTRACT Background The clinical assessment of knee stability after an Anterior Cruciate Ligament (ACL) injury is routinely conducted via operator-dep...
Biomedical knowledge graphs underwrite drug repurposing and clinical decision support, yet the upstream ontologies they depend on update on independen...
Improving long-term memory in artificial neural networks remains an open challenge. To address this, we developed a novel brain-inspired framework for...
We present You Only Stack Once (YOSO), an automated pipeline designed to detect faint, slow-moving Solar System objects in wide-field astronomical sur...
Power-of-two (PoT) quantization significantly reduces the size of deep neural networks (DNNs) and replaces multiplications with bit-shift operations f...
Continual learning (CL) is essential for deploying medical image segmentation models in clinical environments where imaging domains, anatomical target...
Reinforcement learning fine-tuning has become the dominant approach for aligning diffusion models with human preferences. However, assessing images is...
Cross-scene hyperspectral image (HSI) classification stands as a fundamental research topic in remote sensing, with extensive applications spanning va...
While linear-complexity attention mechanisms offer a promising alternative to Softmax attention for overcoming the quadratic bottleneck, training such...
Continuous monitoring of bipolar disorder agitation via voice biomarkers requires disentangling stable speaker traits from volatile affective states o...
Federated learning (FL) holds great potential for medical applications. However, statistical heterogeneity across healthcare institutions poses a majo...
Single-cell foundation models (scFMs) have shown promise as transferable representations of cellular state, but recent zero-shot evaluations suggest t...
Artificial Intelligence (AI) is transforming therapeutic discovery by scoring a large set of promising candidates and prioritizing a shortlist for fur...