AIMC Topic:
Supervised Machine Learning

Clear Filters Showing 1421 to 1430 of 1635 articles

DGCL: dual-graph neural networks contrastive learning for molecular property prediction.

Briefings in bioinformatics
In this paper, we propose DGCL, a dual-graph neural networks (GNNs)-based contrastive learning (CL) integrated with mixed molecular fingerprints (MFPs) for molecular property prediction. The DGCL-MFP method contains two stages. In the first pretraini...

Algorithm-agnostic significance testing in supervised learning with multimodal data.

Briefings in bioinformatics
MOTIVATION: Valid statistical inference is crucial for decision-making but difficult to obtain in supervised learning with multimodal data, e.g. combinations of clinical features, genomic data, and medical images. Multimodal data often warrants the u...

Trainable Reference Spikes Improve Temporal Information Processing of SNNs With Supervised Learning.

Neural computation
Spiking neural networks (SNNs) are the next-generation neural networks composed of biologically plausible neurons that communicate through trains of spikes. By modifying the plastic parameters of SNNs, including weights and time delays, SNNs can be t...

Anatomy-specific Progression Classification in Chest Radiographs via Weakly Supervised Learning.

Radiology. Artificial intelligence
Purpose To develop a machine learning approach for classifying disease progression in chest radiographs using weak labels automatically derived from radiology reports. Materials and Methods In this retrospective study, a twin neural network was devel...

Active Learning Pipeline to Identify Candidate Terms for a CDSS Ontology.

Studies in health technology and informatics
Ontology is essential for achieving health information and information technology application interoperability in the biomedical fields and beyond. Traditionally, ontology construction is carried out manually by human domain experts (HDE). Here, we e...

Weakly Supervised Breast Ultrasound Image Segmentation Based on Image Selection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic segmentation in Breast Ultrasound (BUS) imaging is vital to BUS computer-aided diagnostic systems. Fully supervised learning approaches can attain high accuracy, yet they depend on pixel-level annotations that are challenging to obtain. As ...

Computer vision-inspired contrastive learning for self-supervised anomaly detection in sensor-based remote healthcare monitoring.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Sensor-based remote healthcare monitoring is a promising approach for timely detection of adverse health events such as falls or infections in people living with dementia (PLwD) in the home, and reducing preventable hospital admissions. Current anoma...

CGDM-GAN: An Adversarial Network Approach with Self-supervised Learning for Site Effect Removal.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Imaging data collected from different sites is difficult to pool together due to unwarranted variations introduced by different acquisition protocols or scanners. Data harmonization is an effective way to mitigate site-specific bias while preserving ...

Exploring Self-Supervised Models for Depressive Disorder Detection: A Study on Speech Corpora.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic detection of depressive disorder from speech signals can help improve medical diagnosis reliability. However, a significant challenge in this field is that most of the available depression datasets are relatively small, which limits the eff...

Shared-task Self-supervised Learning for Estimating Free Movement Unified Parkinson's Disease Rating Scale III.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The Unified Parkinson's Disease Rating Scale (UP-DRS) is used to recognize patients with Parkinson's disease (PD) and rate its severity in clinical settings. Machine learning and wearables can reduce the need for clinical examinations and provide a r...