Care of terminally ill / Palliative care

An End-to-End Framework for Joint Denoising and Classification of Hyperspectral Images.

Image denoising and classification are typically conducted separately and sequentially according to their respective objectives. In such a setup, where the two tasks are decoupled, the denoising operation does not optimally serve the classification task and sometimes even deteriorates it. We introduce here a unified deep learning framework for joint denoising and classification of high-dimensional...

High Throughput Read-Across for Screening a Large Inventory of Related Structures by Balancing Artificial Intelligence/Machine Learning and Human Knowledge.

Read-across is an in silico method applied in chemical risk assessment for data-poor chemicals. The read-across outcomes for repeated-dose toxicity end points include the no-observed-adverse-effect level (NOAEL) and estimated uncertainty for a particular category of effects. We have previously developed a new paradigm for estimating NOAELs based on chemoinformatics analysis and experimental study ...

An end-end arrhythmia diagnosis model based on deep learning neural network with multi-scale feature extraction.

This study presents an innovative end-to-end deep learning arrhythmia diagnosis model that aims to address the problems in arrhythmia diagnosis. The m...

Scalability of Cyber-Physical Systems with Real and Virtual Robots in ROS 2.

Nowadays, cyber-physical systems (CPSs) are composed of more and more agents and the demand for designers to develop ever larger multi-agent systems i...

Optimizing Two-Way Partial AUC With an End-to-End Framework.

The Area Under the ROC Curve (AUC) is a crucial metric for machine learning, which evaluates the average performance over all possible True Positive R...

Fully Convolutional Change Detection Framework With Generative Adversarial Network for Unsupervised, Weakly Supervised and Regional Supervised Change Detection.

Deep learning for change detection is one of the current hot topics in the field of remote sensing. However, most end-to-end networks are proposed for...

System Based on Artificial Intelligence Edge Computing for Detecting Bedside Falls and Sleep Posture.

Bedside falls and pressure ulcers are crucial issues in geriatric care. Although many bedside monitoring systems have been proposed, they are limited ...

Gigapixel end-to-end training using streaming and attention.

Current hardware limitations make it impossible to train convolutional neural networks on gigapixel image inputs directly. Recent developments in weak...

Evolving a Pipeline Approach for Abstract Meaning Representation Parsing Towards Dynamic Neural Networks.

Meaning Representation parsing aims to represent a sentence as a structured, Directed, Acyclic Graph (DAG), in an attempt to extract meaning from text...

Prediction of Human Organ Toxicity via Artificial Intelligence Methods.

Unpredicted human organ level toxicity remains one of the major reasons for drug clinical failure. There is a critical need for cost-efficient strateg...

An end-to-end convolutional neural network for automated failure localisation and characterisation of 3D interconnects.

The advancement in the field of 3D integration circuit technology leads to new challenges for quality assessment of interconnects such as through sili...

Comparing end-effector position and joint angle feedback for online robotic limb tracking.

Somatosensation greatly increases the ability to control our natural body. This suggests that supplementing vision with haptic sensory feedback would ...

Prediction of MYCN Gene Amplification in Pediatric Neuroblastomas: Development of a Deep Learning-Based Tool for Automatic Tumor Segmentation and Comparative Analysis of Computed Tomography-Based Radiomics Features Harmonization.

OBJECTIVE: MYCN oncogene amplification is closely linked to high-grade neuroblastoma with poor prognosis. Accurate quantification is essential for ris...

Automatic brain MRI motion artifact detection based on end-to-end deep learning is similarly effective as traditional machine learning trained on image quality metrics.

Head motion artifacts in magnetic resonance imaging (MRI) are an important confounding factor concerning brain research as well as clinical practice. ...

E2EFP-MIL: End-to-end and high-generalizability weakly supervised deep convolutional network for lung cancer classification from whole slide image.

Efficient and accurate distinction of histopathological subtype of lung cancer is quite critical for the individualized treatment. So far, artificial ...

PPSW-SHAP: Towards Interpretable Cell Classification Using Tree-Based SHAP Image Decomposition and Restoration for High-Throughput Bright-Field Imaging.

Advancements in high-throughput microscopy imaging have transformed cell analytics, enabling functionally relevant, rapid, and in-depth bioanalytics w...

Block-level dependency syntax based model for end-to-end aspect-based sentiment analysis.

End-to-End aspect-based sentiment analysis (E2E-ABSA) aims to jointly extract aspect terms and identify their sentiment polarities. Although previous ...

An accelerated end-to-end method for solving routing problems.

The application of neural network models to solve combinatorial optimization has recently drawn much attention and shown promising results in dealing ...

Decoding Silent Speech Based on High-Density Surface Electromyogram Using Spatiotemporal Neural Network.

Finer-grained decoding at a phoneme or syllable level is a key technology for continuous recognition of silent speech based on surface electromyogram ...

Deep learning methods for medical image fusion: A review.

The image fusion methods based on deep learning has become a research hotspot in the field of computer vision in recent years. This paper reviews thes...