Hospital-Based Medicine

Intensivists

Latest AI and machine learning research in intensivists for healthcare professionals.

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Diabetic retinopathy classification using a multi-attention residual refinement architecture.

Diabetic Retinopathy (DR) is a complication caused by diabetes that can destroy the retina, leading ...

Mol-SGGI: an attention-guided comprehensive molecular multi-representation learning and adaptive fusion framework for molecular property prediction.

Molecular property prediction is pivotal for drug discovery, offering significant potential to accel...

Learning spatio-temporal context for basketball action pose estimation with a multi-stream network.

Accurate athlete pose estimation in basketball is crucial for game analysis, player training, and ta...

Synthesized myelin and iron stainings from 7T multi-contrast MRI via deep learning.

Iron and myelin are key biomarkers for studying neurodegenerative and demyelinating brain diseases. ...

How do experts classify sepsis cases for sepsis surveillance? Lessons learned from a Behavioural Artificial Intelligence Technology (BAIT) approach.

OBJECTIVES: To identify relevant objective variables for retrospective identification of 'suspected ...

Multi-fidelity graph neural networks for predicting toluene/water partition coefficients.

Accurate prediction of toluene/water partition coefficients of neutral species is crucial in drug di...

ADAM-DETR: an intelligent rice disease detection method based on adaptive multi-scale feature fusion.

Rice diseases pose a severe threat to global food security, while traditional detection methods suff...

3D IntelliGenes: AI/ML application using multi-omics data for biomarker discovery and disease prediction with multi-dimensional visualization.

BACKGROUND: The cutting-edge artificial intelligence (AI) and machine learning (ML) techniques have ...

Land use classification using multi-year Sentinel-2 images with deep learning ensemble network.

Accurate land use classification is essential for urban planning, environmental monitoring, and agri...

Enhancing B-mode-based breast cancer diagnosis via cross-attention fusion of H-scan and Nakagami imaging with multi-CAM-QUS-driven XAI.

OBJECTIVE: B-mode ultrasound is widely employed for breast lesion diagnosis due to its affordability...

Multi-scale Autoencoder Suppression Strategy for Hyperspectral Image Anomaly Detection.

Autoencoders (AEs) have received extensive attention in hyperspectral anomaly detection (HAD) due to...

ICU-EEG Pattern Detection by a Convolutional Neural Network.

OBJECTIVE: Patients in the intensive care unit (ICU) often require continuous EEG (cEEG) monitoring ...

Predicting post-traumatic stress disorder in relatives of critically ill patients.

PURPOSE OF REVIEW: Symptoms of posttraumatic stress disorder (PTSD) affect up to a third of relative...

Development of a deep learning based approach for multi-material decomposition in spectral CT: a proof of principle in silico study.

Conventional approaches to material decomposition in spectral CT face challenges related to precise ...

Evaluating crowdsourcing for ICU EEG annotation: A comparison with expert performance.

OBJECTIVE: Detection of seizures and rhythmic or periodic patterns (SRPPs) on electroencephalography...

Determination of intrinsic cellular electrical and mechanical properties using micro/nano manipulation techniques.

Single-mode biophysical fingerprints have specific overlap when cell heterogeneity is analyzed. The ...

Semi-supervised medical image segmentation based on multi-stage iterative training and high-confidence pseudo-labeling.

Due to the scarcity and high cost of pixel-level annotations for training data, semi-supervised lear...

Longitudinal big biological data in the AI era.

Generating longitudinal and multi-layered big biological data is crucial for effectively implementin...

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