Latest AI and machine learning research in work force for healthcare professionals.
Human facial images encode a rich spectrum of information, encompassing both stable identity-related traits and mutable attributes such as pose, expression, and emotion. While recent advances in image generation have enabled high-quality identity-conditional face synthesis, precise control over non-identity attributes remains challenging, and disentangling identity from these mutable factors is ...
Algorithmic tools are increasingly used in hiring to improve fairness and diversity, often by enforcing constraints such as gender-balanced candidate shortlists. However, we show theoretically and empirically that enforcing equal representation at the shortlist stage does not necessarily translate into more diverse final hires, even when there is no gender bias in the hiring stage. We identify a...
Coordinated multi-arm manipulation requires satisfying multiple simultaneous geometric constraints across high-dimensional configuration spaces, whi...
Diffusion distillation has emerged as a promising strategy for accelerating text-to-image (T2I) diffusion models by distilling a pretrained score ne...
Text-to-image generation models have achieved remarkable capabilities in synthesizing images, but often struggle to provide fine-grained control ove...
Few-shot classification of hyperspectral images (HSI) faces the challenge of scarce labeled samples. Self-Supervised learning (SSL) and Few-Shot Lea...
Engaging in research during medical training is crucial for fostering critical thinking, enhancing clinical skills, and deepening understanding of med...
Data augmentation for domain-specific image classification tasks often struggles to simultaneously address diversity, faithfulness, and label clarit...
Despite recent advances in text-to-image generation, using synthetically generated data seldom brings a significant boost in performance for supervi...
Background and Objective: Precise preoperative planning and effective physician training for coronary interventions are increasingly important. Desp...
The emergence of unified multimodal understanding and generation models is rapidly attracting attention because of their ability to enhance instruct...
Machine Learning models, more specifically Artificial Neural Networks, are transforming medical imaging by enabling precise liver segmentation, a cruc...
BACKGROUND: The global shortage of mental health professionals, exacerbated by increasing mental health needs post COVID-19, has stimulated growing in...
Metadata, or "data about data," is essential for organizing, understanding, and managing large-scale omics datasets. It enhances data discovery, integ...
: The healthcare sector is under increasing pressure due to an ageing population, rising multimorbidity, and a projected global workforce shortage of ...
Surgical scene segmentation is critical in computer-assisted surgery and is vital for enhancing surgical quality and patient outcomes. Recently, ref...
Machine learning interatomic potentials (MLIPs) offer a promising alternative to traditional force fields and ab initio methods for simulating complex...
Chemical synthesis planning has considerably benefited from advances in the field of machine learning. Neural networks can reliably and accurately pre...
There are some key problems faced in modern agriculture that IoT-based smart farming. These problems such shortage of water, plant diseases, and pest ...
Bed-based pressure-sensitive mats (PSMs) offer a non-intrusive way of monitoring patients during sleep. We focus on four-way sleep position classifi...