AIMC Topic: Deep Learning
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Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging.
NeuroImage
Feb 1, 2025
INTRODUCTION: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian comp...
Detecting living microalgae in ship ballast water based on stained microscopic images and deep learning.
Marine pollution bulletin
Feb 1, 2025
Motivated by the need of rapid detection of living microalgae cells in ship ballast water, this study is intended to determine the activities of microalgae using stained microscopic images and detect the living cells with image processing algorithms....
Deep learning initialized compressed sensing (Deli-CS) in volumetric spatio-temporal subspace reconstruction.
Magma (New York, N.Y.)
Feb 1, 2025
OBJECT: Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or prohibitively burdensome hardware requirements. The aim of this project is to reduce reconstruction...
ERNIE-ac4C: A Novel Deep Learning Model for Effectively Predicting N4-acetylcytidine Sites.
Journal of molecular biology
Feb 1, 2025
RNA modifications are known to play a critical role in gene regulation and cellular processes. Specifically, N4-acetylcytidine (ac4C) modification has emerged as a significant marker involved in mRNA translation efficiency, stability, and various dis...
DOGpred: A Novel Deep Learning Framework for Accurate Identification of Human O-linked Threonine Glycosylation Sites.
Journal of molecular biology
Feb 1, 2025
O-linked glycosylation is a crucial post-translational modification that regulates protein function and biological processes. Dysregulation of this process is associated with various diseases, underscoring the need to accurately identify O-linked gly...
Deep learning assisted prediction of osteogenic capability of orthopedic implant surfaces based on early cell morphology.
Acta biomaterialia
Feb 1, 2025
The surface modification of titanium (Ti) and its alloys is crucial for improving their osteogenic capability, as their bio-inert nature limits effective osseointegration despite their prevalent use in orthopedic implants. However, these modification...
Practical X-ray gastric cancer diagnostic support using refined stochastic data augmentation and hard boundary box training.
Artificial intelligence in medicine
Feb 1, 2025
Endoscopy is widely used to diagnose gastric cancer and has a high diagnostic performance, but it must be performed by a physician, which limits the number of people who can be diagnosed. In contrast, gastric X-rays can be taken by radiographers, thu...
Leveraging deep-learning and unconventional data for real-time surveillance, forecasting, and early warning of respiratory pathogens outbreak.
Artificial intelligence in medicine
Feb 1, 2025
BACKGROUND: Controlling re-emerging outbreaks such as COVID-19 is a critical concern to global health. Disease forecasting solutions are extremely beneficial to public health emergency management. This work aims to design and deploy a framework for r...
ABIET: An explainable transformer for identifying functional groups in biological active molecules.
Computers in biology and medicine
Feb 1, 2025
Recent advancements in deep learning have revolutionized the field of drug discovery, with Transformer-based models emerging as powerful tools for molecular design and property prediction. However, the lack of explainability in such models remains a ...
Biomechanical Risk Classification in Repetitive Lifting Using Multi-Sensor Electromyography Data, Revised National Institute for Occupational Safety and Health Lifting Equation, and Deep Learning.
Biosensors
Feb 1, 2025
Repetitive lifting tasks in occupational settings often result in shoulder injuries, impacting both health and productivity. Accurately assessing the biomechanical risk of these tasks remains a significant challenge in occupational ergonomics, partic...
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