AIMC Topic: Deep Learning

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Development of a predictive model for distant metastasis in HCC patients post-TACE using clinical data, radiomics, and deep learning.

Journal of cancer research and clinical oncology
PURPOSE: Hepatocellular carcinoma (HCC) is a perilous malignant tumor, and transcatheter arterial chemoembolization (TACE) is a widely adopted treatment technique for advanced HCC. Nevertheless, TACE may not effectively reduce the risk of distant met...

Disentangled deep learning method for interior tomographic reconstruction of low-dose x-ray CT.

Physics in medicine and biology
. Low-dose interior tomography integrates low-dose CT (LDCT) with region-of-interest (ROI) imaging which finds wide application in radiation dose reduction and high-resolution imaging. However, the combined effects of noise and data truncation pose g...

Multi-model machine learning for automated identification of rice diseases using leaf image data.

PloS one
Rice, a staple meal for about half of the world's population, is critical to global food security, especially in Asia. However, diseases have a severe impact on rice production, resulting in significant yield losses or outright crop failure. Traditio...

A deep learning approach to gender equality: Forecasting educational indicators with 1D-CNN aligned with SDG 5.

PloS one
Sustainable development goal (SDG) 5 focuses on gender equality and empowerment and it is considered as one of the most important SDGs. Therefore, this article presented a time series prediction model that predicts gender-related educational results ...

Enhanced gallbladder cancer detection via active and self-supervised learning integration: Innovating B-ultrasound image analysis.

PloS one
Gallbladder cancer, a common yet often under diagnosed malignancy, is typically characterized by late detection and a poor prognosis. The rise of deep learning has introduced new methods for its early identification through B-ultrasound imaging, but ...

Enhanced epileptic seizure detection using CNNs with convolutional block attention and short-term memory networks.

Behavioural brain research
Analyzing the electroencephalography (EEG) signals of epilepsy patients can monitor the condition, detect and intervene in epileptic seizures in time. To enhance the lives of these patients, it is necessary to develop accurate methods to detect epile...

MolAI: A Deep Learning Framework for Data-Driven Molecular Descriptor Generation and Advanced Drug Discovery Applications.

Journal of chemical information and modeling
This study introduces MolAI, a robust deep learning model designed for data-driven molecular descriptor generation. Utilizing a vast training data set of 221 million unique compounds, MolAI employs an autoencoder neural machine translation model to g...

Parametrically guided design of beta barrels and transmembrane nanopores using deep learning.

Proceedings of the National Academy of Sciences of the United States of America
Francis Crick's global parameterization of coiled coil geometry has been widely useful for guiding design of new protein structures and functions. However, design guided by similar global parameterization of beta barrel structures has been less succe...

A New Approach to Large Multiomics Data Integration.

Analytical chemistry
Data reduction and data mining are common practices for handling large-scale data from wide-ranging sources, but high-dimensional omics and imaging data sets present difficult challenges for feature extraction and data mining due to the large number ...

MOLECULE: Molecular-dynamics and Optimized deep Learning for Entropy-regularized Classification and Uncertainty-aware Ligand Evaluation.

Journal of chemical theory and computation
Machine learning (ML) and deep learning (DL) methodologies have significantly advanced drug discovery and design in several aspects. Additionally, the integration of structure-based data has proven to successfully support and improve the models' pred...