AIMC Topic: Deep Learning

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Multi-dataset Collaborative Learning for Liver Tumor Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic segmentation of biomedical images has emerged due to its potential in improving real-world clinical processes and has achieved great success in recent years thanks to the development of deep learning. However, it is the limited availability...

An Attention-Based Hybrid Deep Learning Approach for Patient-Specific, Cross-Patient, and Patient-Independent Seizure Detection.

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 epilepsy plays a crucial role in diagnosing and treatment of patients, while most current methods rely on patient-specific models and have shown promising results, which is not suitable for clinical application, especially when...

EEG Emotion Recognition Based on 3D-CTransNet.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Emotion recognition is of great significance for brain-computer interface and emotion computing, and EEG plays a key role in this field. However, the current design of brain computer interface deep learning model is faced with algorithmic or structur...

Unlocking Hidden Risks: Harnessing Artificial Intelligence (AI) to Detect Subclinical Conditions from an Electrocardiogram (ECG).

Journal of insurance medicine (New York, N.Y.)
Recent artificial intelligence (AI) advancements in cardiovascular medicine offer potential enhancements in diagnosis, prediction, treatment, and outcomes. This article aims to provide a basic understanding of AI enabled ECG technology. Specific cond...

Through the Looking Glass Darkly: How May AI Models Influence Future Underwriting?

Journal of insurance medicine (New York, N.Y.)
Applications of Artificial Intelligence (AI) deep-learning models to screening for clinical conditions continue to evolve. Instances provided in this treatise include using a simple one-view PA chest radiograph to screen for Type 2 Diabetes Mellitus ...

Structure-aware deep learning model for peptide toxicity prediction.

Protein science : a publication of the Protein Society
Antimicrobial resistance is a critical public health concern, necessitating the exploration of alternative treatments. While antimicrobial peptides (AMPs) show promise, assessing their toxicity using traditional wet lab methods is both time-consuming...

Using deep learning to improve the intelligibility of a target speaker in noisy multi-talker environments for people with normal hearing and hearing loss.

The Journal of the Acoustical Society of America
Understanding speech in noisy environments is a challenging task, especially in communication situations with several competing speakers. Despite their ongoing improvement, assistive listening devices and speech processing approaches still do not per...

Quantifying Geographic Atrophy in Age-Related Macular Degeneration: A Comparative Analysis Across 12 Deep Learning Models.

Investigative ophthalmology & visual science
PURPOSE: AI algorithms have shown impressive performance in segmenting geographic atrophy (GA) from fundus autofluorescence (FAF) images. However, selection of artificial intelligence (AI) architecture is an important variable in model development. H...

Application of artificial intelligence-based magnetic resonance imaging in diagnosis of cerebral small vessel disease.

CNS neuroscience & therapeutics
Cerebral small vessel disease (CSVD) is an important cause of stroke, cognitive impairment, and other diseases, and its early quantitative evaluation can significantly improve patient prognosis. Magnetic resonance imaging (MRI) is an important method...

Weakly Supervised Deep Learning in Radiology.

Radiology
Deep learning (DL) is currently the standard artificial intelligence tool for computer-based image analysis in radiology. Traditionally, DL models have been trained with strongly supervised learning methods. These methods depend on reference standard...