AIMC Topic: Algorithms

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An Automatic Lie Detection Model Using EEG Signals Based on the Combination of Type 2 Fuzzy Sets and Deep Graph Convolutional Networks.

Sensors (Basel, Switzerland)
In recent decades, many different governmental and nongovernmental organizations have used lie detection for various purposes, including ensuring the honesty of criminal confessions. As a result, this diagnosis is evaluated with a polygraph machine. ...

Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms.

BMC medical research methodology
In contemporary society, depression has emerged as a prominent mental disorder that exhibits exponential growth and exerts a substantial influence on premature mortality. Although numerous research applied machine learning methods to forecast signs o...

MADR-Net: multi-level attention dilated residual neural network for segmentation of medical images.

Scientific reports
Medical image segmentation has made a significant contribution towards delivering affordable healthcare by facilitating the automatic identification of anatomical structures and other regions of interest. Although convolution neural networks have bec...

Automatized self-supervised learning for skin lesion screening.

Scientific reports
Melanoma, the deadliest form of skin cancer, has seen a steady increase in incidence rates worldwide, posing a significant challenge to dermatologists. Early detection is crucial for improving patient survival rates. However, performing total body sc...

Reply: Artificial Intelligence Algorithms Are Not Clairvoyant.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine

Artificial Intelligence Algorithms Are Not Clairvoyant.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine

Privacy-Preserving Synthetic Continual Semantic Segmentation for Robotic Surgery.

IEEE transactions on medical imaging
Deep Neural Networks (DNNs) based semantic segmentation of the robotic instruments and tissues can enhance the precision of surgical activities in robot-assisted surgery. However, in biological learning, DNNs cannot learn incremental tasks over time ...

Deep Learning With Physics-Embedded Neural Network for Full Waveform Ultrasonic Brain Imaging.

IEEE transactions on medical imaging
The convenience, safety, and affordability of ultrasound imaging make it a vital non-invasive diagnostic technique for examining soft tissues. However, significant differences in acoustic impedance between the skull and soft tissues hinder the succes...

ScribFormer: Transformer Makes CNN Work Better for Scribble-Based Medical Image Segmentation.

IEEE transactions on medical imaging
Most recent scribble-supervised segmentation methods commonly adopt a CNN framework with an encoder-decoder architecture. Despite its multiple benefits, this framework generally can only capture small-range feature dependency for the convolutional la...

Hybrid CNN-Transformer Network With Circular Feature Interaction for Acute Ischemic Stroke Lesion Segmentation on Non-Contrast CT Scans.

IEEE transactions on medical imaging
Lesion segmentation is a fundamental step for the diagnosis of acute ischemic stroke (AIS). Non-contrast CT (NCCT) is still a mainstream imaging modality for AIS lesion measurement. However, AIS lesion segmentation on NCCT is challenging due to low c...