AIMC Topic: Deep Learning

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RAUM-GANs: a multi-layer GAN-enhanced framework for accurate multiple sclerosis lesion segmentation in MRI.

Scientific reports
Multiple sclerosis (MS) is a chronic autoimmune disease characterized by inflammatory brain lesions, making MRI-based lesion segmentation challenging due to noise, missing data, and limited availability of high-quality labeled images. This paper pres...

Garden classification of femoral neck fracture using deep-learning algorithm.

Scientific reports
The Garden classification, based on X-ray interpretation and established over 50 years ago, remains the standard clinical classification system for femoral neck fractures (FNFs). Yet, this classification has a high interobserver variability of 70%. W...

Advancing animal behavior recognition with self-supervised pre-training on unlabeled data.

Scientific reports
Deep learning-based animal activity recognition (AAR) achieves promising performance but remains constrained by its reliance on large labeled datasets. While pre-training offers a viable path toward reducing annotation dependency, existing approaches...

Evaluating corticokinematic coherence using electroencephalography and human pose estimation.

Biomedical physics & engineering express
While peripheral mechanisms of proprioception are well understood, the cortical processing of its feedback during dynamic and complex movements remains less clear. Corticokinematic coherence (CKC), which quantifies the coupling between limb movements...

Classification of current density vector map using transformer hybrid residual network.

PloS one
The classification of the current density vector map (CDVM) reconstructed from magnetocardiogram (MCG) is an important indicator for assessing cardiac function and state in clinical diagnosis. Given the limited widespread application of MCG, research...

SEANN: A domain-informed neural network for epidemiological insights.

PloS one
In epidemiology, traditional statistical methods such as logistic regression, linear regression, and other parametric models are commonly employed to investigate associations between predictors and health outcomes. However, non-parametric machine lea...

Vehicle driving area detection and sensor data preprocessing based on deep learning.

PloS one
With the rapid development of intelligent vehicles, it has become particularly important to effectively detect the environment of the vehicle's driving area. A vehicle driving road recognition algorithm on the basis of an improved bilateral segmentat...

SSIF-Affinity: Multimodal Deep Learning of Sequence-Structure Features for Precise Protein-Protein Binding Affinity Prediction.

Journal of chemical information and modeling
Quantitative prediction of binding affinity in protein-protein interactions is critical for deciphering biological mechanisms and advancing therapeutic antibody development. While experimental methods for measuring binding affinity remain limited by ...

NeuroFusionNet: a hybrid EEG feature fusion framework for accurate and explainable Alzheimer's Disease detection.

Scientific reports
Alzheimer's Disease (AD) is a very common neurodegenerative disorders and early detection using electroencephalography (EEG) can enable timely intervention, however, existing computational models often lack robustness, interpretability, and clinical ...

Deep-learning-based non-contrast CT for detecting acute ischemic stroke: a systematic review and HSROC meta-analysis of patient-level diagnostic accuracy.

BMC neurology
BACKGROUND: Non-contrast CT (NCCT) is first-line imaging for suspected acute ischemic stroke (AIS) but has limited early sensitivity; deep learning (DL) may improve patient-level detection.