AIMC Topic: Deep Learning

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Identification and classification of oil and gas pipeline intru-sion events based on 1-D CNN network.

PloS one
Oil and gas pipeline security is critical to national infrastructure, yet existing monitoring systems often lack the sensitivity and real-time responsiveness required to detect subtle intrusion events. This study presents a novel multimodal sensing a...

Brand public opinion data analysis method based on deep learning.

PloS one
With the rapid development of Internet information technology and digital technology, various network platforms and media are showing a vigorous growth momentum. The powerful power of online public opinion has an immeasurable impact on brand awarenes...

A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein-Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases.

Journal of chemical information and modeling
The accurate prediction of protein-ligand binding poses and affinities is central to structure-based drug design. In this study, we first benchmarked three distinct pose generation strategies for data sets from the ASAP Antiviral Challenge 2025: mole...

Deep learning-based MRI model for predicting P53-mutated hepatocellular carcinoma.

BMC medical imaging
BACKGROUND: The P53-mutated Hepatocellular Carcinoma (HCC) is an aggressive variant associated with vascular endothelial growth factor (VEGF) overexpression and increased microvascular density. This study aimed to develop an MRI-based deep learning m...

Deep Learning Approaches for Classifying Children With and Without Autism Spectrum Disorder Using Inertial Measurement Unit Hand Tracking Data: Comparative Study.

JMIR medical informatics
BACKGROUND: Autism spectrum disorder (ASD) is a prevalent neurodevelopmental condition that can be quite difficult to diagnose due to a lack of objective diagnostic methods in the currently used behavioral assessments. Recent work has shown that chil...

Towards trustworthy AI in radiotherapy: a comprehensive review of uncertainty-aware techniques.

Physics in medicine and biology
. Uncertainty quantification (UQ) has emerged as a crucial component in deep learning-based medical image analysis, particularly in radiotherapy (RT). Addressing uncertainty is essential for improving the reliability, interpretability, and clinical a...

Ratio maps of T1w/T2w MRI signal intensity do not improve deep-learning segmentation of pediatric brain tumors.

PloS one
INTRODUCTION: T1w/T2w ratio mapping, combining voxel-wise signal intensities in T1-weighted (T1w) and T2-weighted (T2w) structural MRI, has been used to investigate cortical architecture in the brain, but has also shown promise in tissue discriminati...

EvoZymePro-Cat: A Protein-Ligand-Aware Deep Learning Framework for Predicting Mutation Effects in Enzyme Function.

ACS synthetic biology
Enzymes are biological catalysts that speed up chemical reactions in an eco-friendly way. Precise enzyme design is hindered by vast sequence space and intricate sequence-structure-function interdependencies. To address these challenges, we developed ...

Enhancing the Predictive Power of Macrocyclic Drug Permeability by Knowledge Distillation from Analogous Pretraining Data.

Journal of medicinal chemistry
Macrocyclic drugs offer powerful opportunities for modulating protein-protein interactions, yet their development is limited by poor and unpredictable membrane permeability. Experimental testing is slow, and 3D modeling of macrocycles is computationa...

Non-invasive anemia detection from conjunctiva and sclera images using vision transformer with attention map explainability.

Scientific reports
Iron-deficiency anemia, a prevalent global health issue, traditionally requires invasive procedures for accurate diagnosis, such as a blood sample for measuring hemoglobin (Hgb) concentration. Nevertheless, this marker can be visually assessed by obs...