AIMC Topic: Neural Networks, Computer

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A filter-level explainability framework for CNNs in histopathology image analysis.

Computers in biology and medicine
Convolutional neural networks (CNNs) have achieved remarkable accuracy in histopathology image classification, yet their decision logic remains largely opaque. Most explainability methods, such as Grad-CAM or SHAP, provide only coarse heatmaps, offer...

HGANMDA: A Heterogeneous Graph Adversarial Network for Multimodal Microbe-Drug Association Prediction.

Journal of chemical information and modeling
Accurate prediction of microbe-drug associations (MDAs) is vital for guiding antimicrobial therapy and accelerating drug repositioning. Although experimental validation remains the gold standard, it is costly and time-consuming. Existing models, ofte...

VirMolAnalyte: An AI-Driven Metabolite Annotation Tool.

Analytical chemistry
Metabolites play a crucial role in sustaining biological activities and are also a significant source of new drug development. Nuclear magnetic resonance (NMR) spectroscopy is one of the most important tools for identifying the structures of the meta...

Lightweight Vision Transformer with transfer learning for interpretable Alzheimer's disease severity assessment.

Scientific reports
Early and reliable diagnostic tools are critical for slowing the progression of Alzheimer's disease (AD), a neurodegenerative disorder characterized by memory loss and cognitive decline. This study introduces, ViTTL, lightweight deep learning framewo...

Mixture of checkpoint experts for explainable seizure detection using wearable devices.

Scientific reports
The current gold standard for detecting epileptic seizures is in-hospital video-Electroencephalography (vEEG), but vEEG is resource-intensive and imposes considerable burdens on patients and caregivers. Wearable devices offer an alternative to monito...

Large Separable Kernel Attention-Driven Multidimensional Feature Cross-Level Fusion Classification Network of Knee Cartilage Injury: Algorithm Development and Validation.

JMIR medical informatics
BACKGROUND: Knee cartilage injury (KCI) poses significant challenges in the early clinical diagnosis process, primarily due to its high incidence, the complexity of healing, and the limited sensitivity of initial imaging modalities.

Deep learning-based autonomous retinal vein cannulation in ex vivo porcine eyes.

Science robotics
Retinal vein cannulation (RVC) is an emerging method for treating retinal vein occlusion (RVO). The success of this procedure depends on surgeon expertise and, recently, robotic assistance. This paper proposes an autonomous RVC workflow leveraging de...

Polychromatic neural CBCT reconstruction through density-attenuation modeling.

Physics in medicine and biology
Monochromatic cone beam computed tomography reconstruction algorithms are still most prominent in practice. Since the x-ray detectors of today's machines are mostly energy integrating detectors and thus not able to resolve photon energy levels, recon...

AI-powered printability evaluation framework for 3D bioprinting using Hausdorff distance metrics.

Biofabrication
3D bioprinting enables rapid fabrication of complex biological structures for tissue engineering applications. However, optimizing bioink formulation remains challenging due to complex relationships among material properties, printability, and cell v...

Estimating weaning duration from incremental dentine δ15N and δ13C using a sequence-based LSTM neural network: A deep learning framework for bioarchaeological applications.

PloS one
The estimation of weaning duration from incremental dentine δ15N and δ13C values offers insights into health, nutrition, and demography in past populations. In this study, we developed a novel machine learning approach to estimate weaning duration us...