Hospital-Based Medicine

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

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Multi-path decoder U-Net: A weakly trained real-time segmentation network for object detection and localization in ultrasound scans.

Detecting and localizing an anatomical structure of interest within the field of view of an ultrasou...

Multi-agent medical image segmentation: A survey.

During the last decades, the healthcare area has increasingly relied on medical imaging for the diag...

EchoEFNet: Multi-task deep learning network for automatic calculation of left ventricular ejection fraction in 2D echocardiography.

Left ventricular ejection fraction (LVEF) is essential for evaluating left ventricular systolic func...

A deep learning system for heart failure mortality prediction.

Heart failure (HF) is the final stage of the various heart diseases developing. The mortality rates ...

Deep learning-based open set multi-source domain adaptation with complementary transferability metric for mechanical fault diagnosis.

Intelligent fault diagnosis aims to build robust mechanical condition recognition models with limite...

Exploring a global interpretation mechanism for deep learning networks when predicting sepsis.

The purpose of this study is to identify additional clinical features for sepsis detection through t...

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

BACKGROUND: Recently, deep learning via convolutional neural networks (CNNs) has largely superseded ...

Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning.

It is of great interest to develop and introduce new techniques to automatically and efficiently ana...

Evaluate teaching quality of physical education using a hybrid multi-criteria decision-making framework.

The teaching quality evaluation of physical education is an important measure to promote the profess...

Deep learning based classification of multi-label chest X-ray images via dual-weighted metric loss.

-Thoracic disease, like many other diseases, can lead to complications. Existing multi-label medical...

Automatic vessel crossing and bifurcation detection based on multi-attention network vessel segmentation and directed graph search.

Analysis of the vascular tree is the basic premise to automatically diagnose retinal biomarkers asso...

Multi-attribute decision-making method based on q-rung orthopair probabilistic hesitant fuzzy schweizer-sklar power weighted hamy mean operator.

In order to further improve the computing power of the information aggregation operator in the q-run...

A fair experimental comparison of neural network architectures for latent representations of multi-omics for drug response prediction.

BACKGROUND: Recent years have seen a surge of novel neural network architectures for the integration...

DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics.

Modern oncology offers a wide range of treatments and therefore choosing the best option for particu...

Artificial intelligence in multi-objective drug design.

The factors determining a drug's success are manifold, making de novo drug design an inherently mult...

Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization.

The placenta is crucial to fetal well-being and it plays a significant role in the pathogenesis of h...

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