Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
Functional specialization in continuous systems requires balancing adaptation to environmental stress with the preservation of encoded information. Yet, the physical constraints governing how living systems reconcile selective information retention with energetic structural reorganization remain unclear, a trade-off that may offer transferable design rules for adaptive computing. Here we present a...
Recent studies have explored using pretrained Vision Foundation Models (VFMs) such as DINO for generative autoencoders, showing strong generative performance. Unfortunately, existing approaches often suffer from limited reconstruction fidelity due to the loss of high-frequency details. In this work, we present the DINO Spherical Autoencoder (DINO-SAE), a framework that bridges semantic representat...
Performance degradation due to covariate shift remains a major challenge for deep learning models in medical image segmentation. An open question is w...
Estimating uncertainty in deep learning models is critical for reliable decision-making in high-stakes applications such as medical imaging. Prior res...
Background: Delayed or missed diagnosis of congenital heart disease (CHD) contributes to excess pediatric mortality worldwide. Echocardiography (echo)...
Endometriosis ultrasound reports are often unstructured free-text documents that require manual abstraction for downstream tasks such as analytics, ma...
Cross-modal image translation remains brittle and inefficient. Standard diffusion approaches often rely on a single, global linear transfer between do...
Medical image artificial intelligence models often achieve strong performance in single-center or single-device settings, yet their effectiveness freq...
The growing demand for diverse and high-quality facial datasets for training and testing biometric systems is challenged by privacy regulations, data ...
Representation Autoencoders (RAEs) have shown distinct advantages in diffusion modeling on ImageNet by training in high-dimensional semantic latent sp...
Pupil diameter provides a powerful, non-invasive biomarker of brain state, correlating with arousal, attention, cognitive processing, and level of con...
Supervised deep learning models often achieve excellent performance within their training distribution but struggle to generalize beyond it. In cancer...
Text-Based Person Search (TBPS) has seen significant progress with vision-language models (VLMs), yet it remains constrained by limited training data ...
As a pivotal technique for improving the defense of deep models, adversarial robustness transfer via distillation has demonstrated remarkable success ...
Background: Deep learning algorithms for tuberculosis (TB) screening frequently achieve radiologist-level performance during internal evaluation, yet ...
Diffusion Transformers (DiTs) achieve state-of-the-art performance in text-to-image synthesis but remain computationally expensive due to the iterativ...
We study the online centralized charging scheduling problem (OCCSP). In this problem, a central authority must decide, in real time, when to charge dy...
As the dependence on satellite imaging continues to grow, modern satellites have become increasingly agile, with the new generation, namely super-agil...
Object detectors often perform well in-distribution, yet degrade sharply on a different benchmark. We study cross-dataset object detection (CD-OD) thr...
The utilization of cellulose derivatives offers an eco-friendly alternative to petroleum-based polymers, necessitating research into effective water p...