AIMC Topic: Neural Networks, Computer

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Exploring the Latent Information in Spatial Transcriptomics Data via Multi-View Graph Convolutional Network Based on Implicit Contrastive Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Latest developments in spatial transcriptomics enable thoroughly profiling of gene expression while preserving tissue microenvironment. Connecting gene expression with spatial arrangement is key for precise spatial domain identification, enhancing th...

Automated annotation of virtual dual stains to generate convolutional neural network for detecting cancer metastases in H&E-stained lymph nodes.

Pathology, research and practice
CONTEXT: Staging cancer patients is crucial and requires analyzing all removed lymph nodes microscopically for metastasis. For this pivotal task, convolutional neural networks (CNN) can reduce workload and improve diagnostic accuracy.

Explainable AI for sharp injury identification using transfer learning with pre-trained deep neural networks.

Forensic science international
OBJECTIVE: To investigate an AI-based method for automatically identifying and classifying sharp injuries using deep learning models, evaluate its effectiveness (e.g., accuracy and explainability), and support forensic injury classification.

A mean field theory for pulse-coupled neural oscillators based on the spike time response curve.

Journal of neurophysiology
A mean field method for pulse-coupled oscillators with delays used a self-connected oscillator to represent a synchronous cluster of - 1 oscillators and a single oscillator assumed to be perturbed from the cluster. A periodic train of biexponential ...

Spatio-temporal CNN-BiLSTM dynamic approach to emotion recognition based on EEG signal.

Computers in biology and medicine
In this paper, a hybrid CNN-BiLSTM model for EEG-based emotion detection system is presented. The proposed technique is developed by extracting features using Power Spectral Density (PSD) signal. The proposed approach is carried out by combining CNN ...

WAND: Wavelet Analysis-Based Neural Decomposition of MRS Signals for Artifact Removal.

NMR in biomedicine
Accurate quantification of metabolites in magnetic resonance spectroscopy (MRS) is challenged by low signal-to-noise ratio (SNR), overlapping metabolites, and various artifacts. Particularly, unknown and unparameterized baseline effects obscure the q...

Benchmarking HEp-2 cell segmentation methods in indirect immunofluorescence images - standard models to deep learning.

Computers in biology and medicine
Indirect Immunofluorescence (IIF) stained Human Epithelial (HEp-2) cells are considered the gold standard for detecting autoimmune diseases. Accurate cell segmentation, though often viewed as an intermediary step to downstream tasks like classificati...

Eigenhearts: Cardiac diseases classification using eigenfaces approach.

Computers in biology and medicine
In the realm of cardiovascular medicine, medical imaging plays a crucial role in accurately classifying cardiac diseases and making precise diagnoses. However, the integration of data science techniques in this field presents significant challenges, ...

MEF-Net: Multi-scale and edge feature fusion network for intracranial hemorrhage segmentation in CT images.

Computers in biology and medicine
Intracranial Hemorrhage (ICH) refers to cerebral bleeding resulting from ruptured blood vessels within the brain. Delayed and inaccurate diagnosis and treatment of ICH can lead to fatality or disability. Therefore, early and precise diagnosis of intr...

Faster R-CNN approach for estimating global QRS duration in electrocardiograms with a limited quantity of annotated data.

Computers in biology and medicine
In electrocardiography (ECG), measurement of QRS duration (QRSd) is crucial for diagnosing conditions such as left bundle branch block. To address the limited availability of ECG databases with QRS delineation labels, we present a method to use small...