AIMC Topic: Humans

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DisP+V: A Unified Framework for Disentangling Prototype and Variation From Single Sample per Person.

IEEE transactions on neural networks and learning systems
Single sample per person face recognition (SSPP FR) is one of the most challenging problems in FR due to the extreme lack of enrolment data. To date, the most popular SSPP FR methods are the generic learning methods, which recognize query face images...

Mutual Information-Driven Subject-Invariant and Class-Relevant Deep Representation Learning in BCI.

IEEE transactions on neural networks and learning systems
In recent years, deep learning-based feature representation methods have shown a promising impact on electroencephalography (EEG)-based brain-computer interface (BCI). Nonetheless, owing to high intra- and inter-subject variabilities, many studies on...

Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review.

Journal of healthcare engineering
BACKGROUND: Artificial intelligence (AI) has developed rapidly, and its application extends to clinical decision support system (CDSS) for improving healthcare quality. However, the interpretability of AI-driven CDSS poses significant challenges to w...

Lung Diseases Detection Using Various Deep Learning Algorithms.

Journal of healthcare engineering
The primary objective of this proposed framework work is to detect and classify various lung diseases such as pneumonia, tuberculosis, and lung cancer from standard X-ray images and Computerized Tomography (CT) scan images with the help of volume dat...

Adversarial Knowledge Distillation Based Biomedical Factoid Question Answering.

IEEE/ACM transactions on computational biology and bioinformatics
Biomedical factoid question answering is an essential application for biomedical information sharing. Recently, neural network based approaches have shown remarkable performance for this task. However, due to the scarcity of annotated data which requ...

DeepSide: A Deep Learning Approach for Drug Side Effect Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Drug failures due to unforeseen adverse effects at clinical trials pose health risks for the participants and lead to substantial financial losses. Side effect prediction algorithms have the potential to guide the drug design process. LINCS L1000 dat...

Clinical utility of a deep-learning mortality prediction model for cardiac surgery decision making.

The Journal of thoracic and cardiovascular surgery
OBJECTIVES: The aim of this study using decision curve analysis (DCA) was to evaluate the clinical utility of a deep-learning mortality prediction model for cardiac surgery decision making compared with the European System for Cardiac Operative Risk ...

DeepSWI: Using Deep Learning to Enhance Susceptibility Contrast on T2*-Weighted MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Although susceptibility-weighted imaging (SWI) is the gold standard for visualizing cerebral microbleeds (CMBs) in the brain, the required phase data are not always available clinically. Having a postprocessing tool for generating SWI con...

Implementation of artificial intelligence in thoracic imaging-a what, how, and why guide from the European Society of Thoracic Imaging (ESTI).

European radiology
This statement from the European Society of Thoracic imaging (ESTI) explains and summarises the essentials for understanding and implementing Artificial intelligence (AI) in clinical practice in thoracic radiology departments. This document discusses...

Accurate stratification between VEXAS syndrome and differential diagnoses by deep learning analysis of peripheral blood smears.

Clinical chemistry and laboratory medicine
OBJECTIVES: VEXAS syndrome is a newly described autoinflammatory disease associated with somatic mutations and vacuolization of myeloid precursors. This disease possesses an increasingly broad spectrum, leading to an increase in the number of suspec...