Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
Medical imaging models frequently fail when deployed across hospitals, scanners, populations, or imaging protocols due to domain shift, limiting their clinical reliability. While transfer learning and domain adaptation address such shifts statistically, they often rely on spurious correlations that break under changing conditions. On the other hand, causal inference provides a principled way to id...
Advances in diffusion-based video generation models, while significantly improving human animation, poses threats of misuse through the creation of fake videos from a specific person's photo and text prompts. Recent efforts have focused on adversarial attacks that introduce crafted perturbations to protect images from diffusion-based models. However, most existing approaches target image generatio...
Background: Cardiovascular disease (CVD) prevention is limited by the major challenge of low long-term adherence to effective lifestyle regimens. Arte...
Transformer-based Genomic Language Models (GLMs) have achieved strong performance across diverse genomic prediction tasks. However, their tendency tow...
Generating rare compositional concepts in text-to-image synthesis remains a challenge for diffusion models, particularly for attributes that are uncom...
Large vision-language models (VLMs) often exhibit weakened safety alignment with the integration of the visual modality. Even when text prompts contai...
A fundamental bottleneck in Novel View Synthesis (NVS) for autonomous driving is the inherent supervision gap on novel trajectories: models are tasked...
Image-conditioned Video diffusion models achieve impressive visual realism but often suffer from weakened motion fidelity, e.g., reduced motion dynami...
Efficiently managing and utilizing large-scale medical imaging datasets with limited resources presents significant challenges. While coreset selectio...
YOLO detectors are known for their fast inference speed, yet training them remains unexpectedly time-consuming due to their exhaustive pipeline that p...
The transition from image to video understanding requires vision-language models (VLMs) to shift from recognizing static patterns to reasoning over te...
In practical machine learning, the environments encountered during the model development and deployment phases often differ, especially when a model i...
Predicting drug response in patients from preclinical data remains a major challenge in precision oncology due to the substantial biological gap betwe...
Recent advances in one-step generative frameworks, such as flow map models, have significantly improved the efficiency of image generation by learning...
Tabular data have been playing a mostly important role in diverse real-world fields, such as healthcare, engineering, finance, etc. The recent success...
Synapses are the fundamental units of neural computation, yet quantifying their organization across circuit-level scales remains a critical bottleneck...
Automated diagnosis from chest computed tomography (CT) scans faces two persistent challenges in clinical deployment: distribution shift across acquis...
The progressive automation of transport promises to enhance safety and sustainability through shared mobility. Like other vehicles and road users, and...
Diffusion Transformers (DiTs) have demonstrated remarkable scalability and quality in image and video generation, prompting growing interest in extend...
Increasing staffing constraints and turnaround-time pressures in Prior authorization (PA) have led to increasing automation of decision systems to sup...