Hospital-Based Medicine

Intensivists

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

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Quaternion Cross-Modality Spatial Learning for Multi-Modal Medical Image Segmentation.

Recently, the Deep Neural Networks (DNNs) have had a large impact on imaging process including medic...

A New Multi-Atlas Based Deep Learning Segmentation Framework With Differentiable Atlas Feature Warping.

Deep learning based multi-atlas segmentation (DL-MA) has achieved the state-of-the-art performance i...

Echoes of images: multi-loss network for image retrieval in vision transformers.

This paper introduces a novel approach to enhance content-based image retrieval, validated on two be...

Attributed Multi-Order Graph Convolutional Network for Heterogeneous Graphs.

Heterogeneous graph neural networks play a crucial role in discovering discriminative node embedding...

A Hybrid Framework of Dual-Domain Signal Restoration and Multi-depth Feature Reinforcement for Low-Dose Lung CT Denoising.

Low-dose computer tomography (LDCT) has been widely used in medical diagnosis. Various denoising met...

Enhancing neural encoding models for naturalistic perception with a multi-level integration of deep neural networks and cortical networks.

Cognitive neuroscience aims to develop computational models that can accurately predict and explain ...

Skin-Inspired Multi-Modal Mechanoreceptors for Dynamic Haptic Exploration.

Active sensing is a fundamental aspect of human and animal interactions with the environment, provid...

moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud.

Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics prof...

Opening Pandora's box by generating ICU diaries through artificial intelligence: A hypothetical study protocol.

BACKGROUND: Patients and families on Intensive Care Units (ICU) benefit from ICU diaries, enhancing ...

Kinome-Wide Virtual Screening by Multi-Task Deep Learning.

Deep learning is a machine learning technique to model high-level abstractions in data by utilizing ...

A multi-branch convolutional neural network for snoring detection based on audio.

Obstructive sleep apnea (OSA) is associated with various health complications, and snoring is a prom...

Machine learning models to evaluate mortality in pediatric patients with pneumonia in the intensive care unit.

OBJECTIVES: This study aimed to predict mortality in children with pneumonia who were admitted to th...

scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data.

Single-cell RNA sequencing (scRNA-seq) data reveal the complexity and diversity of cellular ecosyste...

Overcoming the Challenge of Accurate Segmentation of Lung Nodules: A Multi-crop CNN Approach.

Lung nodules are generated based on the growth of small and round- or oval-shaped cells in the lung,...

Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy.

Fluorescent Neuronal Cells v2 is a collection of fluorescence microscopy images and the correspondin...

Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network.

Deep learning recently achieved advancement in the segmentation of medical images. In this regard, U...

Neuromonitoring in the ICU - what, how and why?

PURPOSE OF REVIEW: We selectively review emerging noninvasive neuromonitoring techniques and the evi...

Multi-modal deep learning networks for RGB-D pavement waste detection and recognition.

To create a clean living environment, governments around the world have hired a large number of work...

High-precision retinal blood vessel segmentation based on a multi-stage and dual-channel deep learning network.

The high-precision segmentation of retinal vessels in fundus images is important for the early diagn...

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