AIMC Topic: Neural Networks, Computer

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Self-Supervised Graph Representation Learning for Single-Cell Classification.

Interdisciplinary sciences, computational life sciences
Accurately identifying cell types in single-cell RNA sequencing data is critical for understanding cellular differentiation and pathological mechanisms in downstream analysis. As traditional biological approaches are laborious and time-intensive, it ...

A fluorescence probe-smartphone-machine learning integrated platform for the visual and intelligent detection of imidacloprid in agricultural products.

Food chemistry
Imidacloprid is a pesticide commonly used in agriculture production. Portable and accurate detection of imidacloprid residues is of great significance to food safety and human health. Herein, a red-emitting rare earth complex (Eu-IMDC) probe is prepa...

Localize-diffusion based dual-branch anomaly detection.

Neural networks : the official journal of the International Neural Network Society
Due to the scarcity of real anomaly samples for use in anomaly detection studies, data augmentation methods are typically employed to generate pseudo anomaly samples to supplement the limited real samples. However, existing data augmentation methods ...

FSDM: An efficient video super-resolution method based on Frames-Shift Diffusion Model.

Neural networks : the official journal of the International Neural Network Society
Video super-resolution is a fundamental task aimed at enhancing video quality through intricate modeling techniques. Recent advancements in diffusion models have significantly enhanced image super-resolution processing capabilities. However, their in...

Predicting cell cycle stage from 3D single-cell nuclear-stained images.

Life science alliance
The cell cycle governs the proliferation of all eukaryotic cells. Profiling cell cycle dynamics is therefore central to basic and biomedical research. However, current approaches to cell cycle profiling involve complex interventions that may confound...

Prediction of IUGR condition at birth by means of CTG recordings and a ResNet model.

Computers in biology and medicine
OBJECTIVE: Sub-optimal uterine-placental perfusion and fetal nutrition can lead to intrauterine growth restriction (IUGR), also called fetal growth restriction (FGR). Antenatal cardiotocography (CTG) can aid in the early detection of IUGR. Reliably d...

FET-UNet: Merging CNN and transformer architectures for superior breast ultrasound image segmentation.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Breast cancer remains a significant cause of mortality among women globally, highlighting the critical need for accurate diagnosis. Although Convolutional Neural Networks (CNNs) have shown effectiveness in segmenting breast ultrasound images...

A Neural Network Model for Intelligent Classification of Distal Radius Fractures Using Statistical Shape Model Extraction Features.

Orthopaedic surgery
OBJECTIVE: Distal radius fractures account for 12%-17% of all fractures, with accurate classification being crucial for proper treatment planning. Studies have shown that in emergency settings, the misdiagnosis rate of hand/wrist fractures can reach ...

3D Hyperspectral Data Analysis with Spatially Aware Deep Learning for Diagnostic Applications.

Analytical chemistry
Nowadays, with the rise of artificial intelligence (AI), deep learning algorithms play an increasingly important role in various traditional fields of research. Recently, these algorithms have already spread into data analysis for Raman spectroscopy....

Application of Deep Learning to Predict the Persistence, Bioaccumulation, and Toxicity of Pharmaceuticals.

Journal of chemical information and modeling
This study investigates the application of a deep learning (DL) model, specifically a message-passing neural network (MPNN) implemented through Chemprop, to predict the persistence, bioaccumulation, and toxicity (PBT) characteristics of compounds, wi...