Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
In practical machine learning, the environments encountered during the model development and deploym...
Predicting drug response in patients from preclinical data remains a major challenge in precision on...
Recent advances in one-step generative frameworks, such as flow map models, have significantly impro...
Tabular data have been playing a mostly important role in diverse real-world fields, such as healthc...
Synapses are the fundamental units of neural computation, yet quantifying their organization across ...
Automated diagnosis from chest computed tomography (CT) scans faces two persistent challenges in cli...
Diffusion Transformers (DiTs) have demonstrated remarkable scalability and quality in image and vide...
Increasing staffing constraints and turnaround-time pressures in Prior authorization (PA) have led t...
Machine unlearning (MU) addresses privacy risks in pretrained models. The main goal of MU is to remo...
Weakly Supervised Object Localization (WSOL) models enable joint classification and region-of-intere...
Multimodal Large Language Models (MLLMs) enable interpretable multimedia forensics by generating tex...
Vision-language models (VLMs) face significant computational inefficiencies caused by excessive gene...
While Diffusion Models excel in text-to-image synthesis, they often suffer from concept omission whe...
Vision-language models (VLMs) face significant computational inefficiencies caused by excessive gene...
Thyroid ultrasound (US) automation couples two competing requirements: global, geometry-driven reaso...
Contrastive learning has become a fundamental approach in both uni-modal and multi-modal frameworks....
Sketch colorization is a critical task for automating and assisting in the creation of animations an...
Interactive segmentation enables clinicians to guide annotation, but existing zero-shot models like ...
Strong semantic representations improve the convergence and generation quality of diffusion and flow...
This study presents dAMN, a hybrid neural-mechanistic model that integrates neural networks with gen...
Accurate polyp segmentation from colonoscopy images is critical for colorectal cancer prevention, ye...