Practice Management

Staffing & Scheduling

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

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MHS U-Net: Multi-scale hybrid subtraction network for medical image segmentation.

Medical image segmentation plays a critical role in modern clinical diagnosis. However, existing met...

Diagnostic tools in respiratory medicine (Review).

Recent advancements in diagnostic technologies have significantly transformed the landscape of respi...

Machine learning in biofluid mechanics: A review of recent developments.

This review paper comprehensively examines recent advancements in machine learning (ML) applications...

Weakly-supervised semantic segmentation in histology images using contrastive learning and self-training.

This paper presents a novel method for weakly-supervised semantic segmentation (WSSS) of histology i...

Extracellular vesicles as nature's nano carriers in cancer therapy: Insights toward preclinical studies and clinical applications.

Extracellular vesicles (EVs), which are secreted by various cell types, hold significant potential f...

Deep implicit optimization enables robust learnable features for deformable image registration.

Deep Learning in Image Registration (DLIR) methods have been tremendously successful in image regist...

Neural Networks for On-Chip Model Predictive Control: A Method to Build Optimized Training Datasets and its Application to Type-1 Diabetes.

Training neural networks (NNs) to behave as model predictive control (MPC) algorithms is an effectiv...

Industrial Robotics and the Future of Work.

Starting in the 1970s with robots that were physically isolated from contact with their human co-wor...

Pushing the Limit of Post-Training Quantization.

Recently, post-training quantization (PTQ) has become the de facto way to produce efficient low-prec...

Impact of Noisy Supervision in Foundation Model Learning.

Foundation models are usually pre-trained on large-scale datasets and then adapted to different down...

Generating Inverse Feature Space for Class Imbalance in Point Cloud Semantic Segmentation.

Point cloud semantic segmentation can enhance the understanding of the production environment and is...

EBM-WGF: Training energy-based models with Wasserstein gradient flow.

Energy-based models (EBMs) show their efficiency in density estimation. However, MCMC sampling in tr...

Hard-Aware Instance Adaptive Self-Training for Unsupervised Cross-Domain Semantic Segmentation.

The divergence between labeled training data and unlabeled testing data is a significant challenge f...

Self-training EEG discrimination model with weakly supervised sample construction: An age-based perspective on ASD evaluation.

Deep learning for Electroencephalography (EEG) has become dominant in the tasks of discrimination an...

FedELR: When federated learning meets learning with noisy labels.

Existing research on federated learning (FL) usually assumes that training labels are of high qualit...

Quantum federated learning with pole-angle quantum local training and trainable measurement.

Recently, quantum federated learning (QFL) has received significant attention as an innovative parad...

Multi-Task Learning in Homogeneous Catalysis: A Case Study for Predicting the Catalytic Performance in Ethylene Polymerization.

This study focuses on training a multi-task learning (MTL) type machine learning (ML) model to predi...

Prediction of remaining surgery duration based on machine learning methods and laparoscopic annotation data.

OBJECTIVES: The operating room is a fast-paced and demanding environment. Among the various factors ...

Machine learning-driven insights into retention mechanism in IAM chromatography of anticancer sulfonamides: Implications for biological efficacy.

Machine learning (ML) tools offer new opportunities in drug discovery, especially for enhancing our ...

OCT in dermatology: a process for determining whether a fully diversified dataset is needed for AI model-building.

Optical coherence tomography (OCT) has sufficient depth penetration for detection of skin pathologie...

Real-time integrated modeling of soft tissue deformation and stress based on deep learning.

. Accurately and in real-time simulating soft tissue deformation and visualizing stress distribution...

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