Intensivists

Deformable registration of lung 3DCT images using an unsupervised heterogeneous multi-resolution neural network.

Lung image registration is more challenging than other organs. This is because the breath of the hum...

Multi-event survival analysis through dynamic multi-modal learning for ICU mortality prediction.

BACKGROUND AND OBJECTIVE: Survival analysis is widely applied for assessing the expected duration of...

Gait-CNN-ViT: Multi-Model Gait Recognition with Convolutional Neural Networks and Vision Transformer.

Gait recognition, the task of identifying an individual based on their unique walking style, can be ...

The comforting companion: using AI to bring loved one's voices to newborns, infants, and unconscious patients in ICU.

A Multidatabase ExTRaction PipEline (METRE) for facile cross validation in critical care research.

Transforming raw EHR data into machine learning model-ready inputs requires considerable effort. One...

Multi-View Graph Neural Architecture Search for Biomedical Entity and Relation Extraction.

Recently, graph neural architecture search (GNAS) frameworks have been successfully used to automati...

A Multi-Attention Approach for Person Re-Identification Using Deep Learning.

Person re-identification (Re-ID) is a method for identifying the same individual via several non-int...

A hybrid model- and deep learning-based framework for functional lung image synthesis from multi-inflation CT and hyperpolarized gas MRI.

BACKGROUND: Hyperpolarized gas MRI is a functional lung imaging modality capable of visualizing regi...

CSI-Based Human Activity Recognition Using Multi-Input Multi-Output Autoencoder and Fine-Tuning.

Wi-Fi-based human activity recognition (HAR) has gained considerable attention recently due to its e...

De novo drug design based on Stack-RNN with multi-objective reward-weighted sum and reinforcement learning.

CONTEXT: In recent decades, drug development has become extremely important as different new disease...

Enhancing Multi-disease Diagnosis of Chest X-rays with Advanced Deep-learning Networks in Real-world Data.

The current artificial intelligence (AI) models are still insufficient in multi-disease diagnosis fo...

Multi CNN based automatic detection of mitotic nuclei in breast histopathological images.

In breast cancer diagnosis, the number of mitotic cells in a specific area is an important measure. ...

Multi-modal body part segmentation of infants using deep learning.

BACKGROUND: Monitoring the body temperature of premature infants is vital, as it allows optimal temp...

Improvement of multi-task learning by data enrichment: application for drug discovery.

Multi-task learning in deep neural networks has become a topic of growing importance in many researc...

Co-model for chemical toxicity prediction based on multi-task deep learning.

The toxicity of compounds is closely related to the effectiveness and safety of drug development, an...

A multi-task and multi-channel convolutional neural network for semi-supervised neonatal artefact detection.

. Automated artefact detection in the neonatal electroencephalogram (EEG) is crucial for reliable au...

Novel architecture for gated recurrent unit autoencoder trained on time series from electronic health records enables detection of ICU patient subgroups.

Electronic health records (EHRs) are used in hospitals to store diagnoses, clinician notes, examinat...

CustOmics: A versatile deep-learning based strategy for multi-omics integration.

The availability of patient cohorts with several types of omics data opens new perspectives for expl...

MNAS: Multi-Scale and Multi-Level Memory-Efficient Neural Architecture Search for Low-Dose CT Denoising.

Lowering the radiation dose in computed tomography (CT) can greatly reduce the potential risk to pub...

Towards artificial intelligence to multi-omics characterization of tumor heterogeneity in esophageal cancer.

Esophageal cancer is a unique and complex heterogeneous malignancy, with substantial tumor heterogen...

Editorial for "Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor and Multi-disease Study".