Practice Management

Staffing & Scheduling

Latest AI and machine learning research in staffing & scheduling for healthcare professionals.

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Towards Realistic Low-Light Image Enhancement via ISP Driven Data Modeling

Deep neural networks (DNNs) have recently become the leading method for low-light image enhancemen...

Predictive Multiplicity in Survival Models: A Method for Quantifying Model Uncertainty in Predictive Maintenance Applications

In many applications, especially those involving prediction, models may yield near-optimal perform...

Recommending Clinical Trials for Online Patient Cases using Artificial Intelligence

Clinical trials are crucial for assessing new treatments; however, recruitment challenges - such a...

Efficient Distributed Retrieval-Augmented Generation for Enhancing Language Model Performance

Small language models (SLMs) support efficient deployments on resource-constrained edge devices, b...

Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg

Magnetic resonance imaging (MRI) has played a crucial role in fetal neurodevelopmental research. S...

Balancing Stability and Plasticity in Pretrained Detector: A Dual-Path Framework for Incremental Object Detection

The balance between stability and plasticity remains a fundamental challenge in pretrained model-b...

AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification

Time series classification (TSC) is an important task in time series analysis. Existing TSC method...

Early-Bird Diffusion: Investigating and Leveraging Timestep-Aware Early-Bird Tickets in Diffusion Models for Efficient Training

Training diffusion models (DMs) requires substantial computational resources due to multiple forwa...

Reduction of Supervision for Biomedical Knowledge Discovery

Knowledge discovery is hindered by the increasing volume of publications and the scarcity of exten...

Bayesian Cross-Modal Alignment Learning for Few-Shot Out-of-Distribution Generalization

Recent advances in large pre-trained models showed promising results in few-shot learning. However...

SpecEE: Accelerating Large Language Model Inference with Speculative Early Exiting

Early exiting has recently emerged as a promising technique for accelerating large language models...

Enhancing knowledge retention for continual learning with domain-specific adapters and features gating

Continual learning empowers models to learn from a continuous stream of data while preserving prev...

Leveraging Synthetic Adult Datasets for Unsupervised Infant Pose Estimation

Human pose estimation is a critical tool across a variety of healthcare applications. Despite sign...

Tuning-Free Image Editing with Fidelity and Editability via Unified Latent Diffusion Model

Balancing fidelity and editability is essential in text-based image editing (TIE), where failures ...

Exploring Kernel Transformations for Implicit Neural Representations

Implicit neural representations (INRs), which leverage neural networks to represent signals by map...

CATS: Mitigating Correlation Shift for Multivariate Time Series Classification

Unsupervised Domain Adaptation (UDA) leverages labeled source data to train models for unlabeled t...

Progressive Multi-Source Domain Adaptation for Personalized Facial Expression Recognition

Personalized facial expression recognition (FER) involves adapting a machine learning model using ...

FaR: Enhancing Multi-Concept Text-to-Image Diffusion via Concept Fusion and Localized Refinement

Generating multiple new concepts remains a challenging problem in the text-to-image task. Current ...

Symbiotic AI: Augmenting Human Cognition from PCs to Cars

As AI takes on increasingly complex roles in human-computer interaction, fundamental questions ari...

ESC: Erasing Space Concept for Knowledge Deletion

As concerns regarding privacy in deep learning continue to grow, individuals are increasingly appr...

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