AIMC Topic: Deep Learning

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Optimizing network bandwidth slicing identification: NADAM-enhanced CNN and VAE data preprocessing for enhanced interpretability.

PloS one
Communication networks of the future will rely heavily on network slicing (NS), a technology that enables the creation of distinct virtual networks within a shared physical infrastructure. This capability is critical for meeting the diverse quality o...

Cell Decoder: decoding cell identity with multi-scale explainable deep learning.

Genome biology
BACKGROUND: Cells are the fundamental units of life, and understanding their diversity and functionality requires detailed characterization. The rise of single-cell omics data enables this, yet current deep learning approaches lack multi-scale interp...

Automated assessment of periapical health based on the radiographic periapical index using YOLOv8, YOLOv11, and YOLOv12 one-stage object detection algorithms.

Scientific reports
This study investigates the application of recent YOLO (You Only Look Once) algorithms for automated detection and classification of apical periodontitis using the Periapical Index (PAI) scoring system (1-5). A dataset of 699 digital periapical radio...

Multidimensional trophoblast invasion assessment by combining 3D in vitro modeling and deep learning analysis.

NPJ systems biology and applications
Infertility affects millions of couples worldwide, and in vitro fertilization is a key therapeutic strategy for achieving parenthood. Despite advances, the first IVF attempt fails in ~60% of patients, highlighting the need for innovative solutions to...

Rapid Liver Fibrosis Evaluation Using the UNet-ResNet50-32 × 4d Model in Magnetic Resonance Elastography: Retrospective Study.

JMIR medical informatics
BACKGROUND: Liver fibrosis is a pathological outcome of chronic liver injury and a hallmark of multiple chronic liver diseases. Magnetic resonance elastography (MRE) provides a non-invasive modality for evaluating the severity of liver fibrosis.

A microneedle-based integrated three-electrode system for pesticide detection using machine learning.

The Analyst
Pesticides contribute to enhanced agricultural productivity, yet excessive residues pose significant health risks to humans as they persist even after washing, making their detection in crops critically important. In this study, we employed 3D-printe...

MODAPro: Explainable Heterogeneous Networks with Variational Graph Autoencoder for Mining Disease-Specific Functional Molecules and Pathways from Omics Data.

Analytical chemistry
The rapid growth of multiomics data has revolutionized our ability to investigate disease mechanisms, yet significant challenges persist in achieving meaningful integration due to inherent data heterogeneity, characteristic sparsity patterns, and the...

Automated Multimodal Image Registration for Prostate Cancer Using Squeeze-and-Excitation ResNet with Thin Plate Spline Transformation: A Deep Learning Approach.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Accurate spatial correlation between preoperative prostate MRI and post-prostatectomy histopathology is critical for improving prostate cancer diagnosis, treatment planning, and MRI interpretation. Current manual registration methods are t...

An Explainable 3D-Deep Learning Model for EEG Decoding in Brain-Computer Interface Applications.

International journal of neural systems
Decoding electroencephalographic (EEG) signals is of key importance in the development of brain-computer interface (BCI) systems. However, high inter-subject variability in EEG signals requires user-specific calibration, which can be time-consuming a...

Germline-aware deep learning models and benchmarks for predicting antibody VH-VL pairing.

mAbs
Variable heavy (VH) and variable light (VL) chain pairing is a critical determinant of antibody diversity, stability, and antigen-binding specificity. Identifying productive VH - VL combinations experimentally is labor-intensive and costly, motivatin...