AIMC Topic: Algorithms

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Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep learning has presented great potential in accurate MR image segmentation when enough labeled data are provided for network optimization. However, manually annotating three-dimensional (3D) MR images is tedious and time-consuming, req...

A Navigation Algorithm to Enable Sustainable Control of Insect-Computer Hybrid Robot with Stimulus Signal Regulator and Habituation-Breaking Function.

Soft robotics
The insect-computer hybrid soft robots are receiving increasing attention due to their excellent motor capabilities, small size, and low power consumption. However, the effective control of insects is limited to minutes since the response from insect...

Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review.

Computers in biology and medicine
Electrocardiogram (ECG) are the physiological signals and a standard test to measure the heart's electrical activity that depicts the movement of cardiac muscles. A review study has been conducted on ECG signals analysis with the help of artificial i...

Modified Meta Heuristic BAT with ML Classifiers for Detection of Autism Spectrum Disorder.

Biomolecules
ASD (autism spectrum disorder) is a complex developmental and neurological disorder that impacts the social life of the affected person by disturbing their capability for interaction and communication. As it is a behavioural disorder, early treatment...

Developing a Machine Learning Algorithm to Predict the Probability of Medical Staff Work Mode Using Human-Smartphone Interaction Patterns: Algorithm Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Traditional methods for investigating work hours rely on an employee's physical presence at the worksite. However, accurately identifying break times at the worksite and distinguishing remote work outside the worksite poses challenges in ...

Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians.

Nature biomedical engineering
The inferences of most machine-learning models powering medical artificial intelligence are difficult to interpret. Here we report a general framework for model auditing that combines insights from medical experts with a highly expressive form of exp...

Optimization-enabled deep learning model for disease detection in IoT platform.

Network (Bristol, England)
Nowadays, Internet of things (IoT) and IoT platforms are extensively utilized in several healthcare applications. The IoT devices produce a huge amount of data in healthcare field that can be inspected on an IoT platform. In this paper, a novel algor...

Differentiating ChatGPT-Generated and Human-Written Medical Texts: Quantitative Study.

JMIR medical education
BACKGROUND: Large language models, such as ChatGPT, are capable of generating grammatically perfect and human-like text content, and a large number of ChatGPT-generated texts have appeared on the internet. However, medical texts, such as clinical not...

RVCNet: A hybrid deep neural network framework for the diagnosis of lung diseases.

PloS one
Early evaluation and diagnosis can significantly reduce the life-threatening nature of lung diseases. Computer-aided diagnostic systems (CADs) can help radiologists make more precise diagnoses and reduce misinterpretations in lung disease diagnosis. ...

Deep-Learning-Based MRI Microbleeds Detection for Cerebral Small Vessel Disease on Quantitative Susceptibility Mapping.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Cerebral microbleeds (CMB) are indicators of severe cerebral small vessel disease (CSVD) that can be identified through hemosiderin-sensitive sequences in MRI. Specifically, quantitative susceptibility mapping (QSM) and deep learning were...