AI Medical Compendium Topic

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Transformer-based temporal sequence learners for arrhythmia classification.

Medical & biological engineering & computing
An electrocardiogram (ECG) plays a crucial role in identifying and classifying cardiac arrhythmia. Traditional methods employ handcrafted features, and more recently, deep learning methods use convolution and recursive structures to classify heart si...

A forensic evaluation method for DeepFake detection using DCNN-based facial similarity scores.

Forensic science international
Detecting DeepFake videos has become a central task in modern multimedia forensics applications. This article presents a method to detect face swapped videos when the portrayed person in the video is known. We propose using a threshold classifier bas...

Transfer learning enables predictions in network biology.

Nature
Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recen...

Machine Learning and Electroencephalogram Signal based Diagnosis of Dipression.

Neuroscience letters
Depression is a psychological condition which hampers day to day activity (Thinking, Feeling or Action). The early detection of this illness will help to save many lives because it is now recognized as a global problem which could even lead to suicid...

Learnable latent embeddings for joint behavioural and neural analysis.

Nature
Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data increases, there is growing interest in modelling neural dynamics during adaptive behaviours to probe neu...

MuRCL: Multi-Instance Reinforcement Contrastive Learning for Whole Slide Image Classification.

IEEE transactions on medical imaging
Multi-instance learning (MIL) is widely adop- ted for automatic whole slide image (WSI) analysis and it usually consists of two stages, i.e., instance feature extraction and feature aggregation. However, due to the "weak supervision" of slide-level l...

Four-Dimensional Cone Beam CT Imaging Using a Single Routine Scan via Deep Learning.

IEEE transactions on medical imaging
A novel method is proposed to obtain four-dimensional (4D) cone-beam computed tomography (CBCT) images from a routine scan in patients with upper abdominal cancer. The projections are sorted according to the location of the lung diaphragm before bein...

Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Previous work on clinical relation extraction from free-text sentences leveraged information about semantic types from clinical knowledge bases as a part of entity representations. In this paper, we exploit additional evidence by also making use of ....

Foundation models for generalist medical artificial intelligence.

Nature
The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI) models is likely to usher in newfound capabilities in medicine. We propose a new paradigm for medical AI, which we refer to as generalist medical AI (GMAI)....

Utilization of Deep Convolutional Neural Networks for Accurate Chest X-Ray Diagnosis and Disease Detection.

Interdisciplinary sciences, computational life sciences
Chest radiography is a widely used diagnostic imaging procedure in medical practice, which involves prompt reporting of future imaging tests and diagnosis of diseases in the images. In this study, a critical phase in the radiology workflow is automat...