AIMC Topic: Deep Learning

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CatNet: Sequence-based deep learning with cross-attention mechanism for identifying endocrine-disrupting chemicals.

Journal of hazardous materials
Endocrine-disrupting chemicals (EDCs) pose significant environmental and health risks due to their potential to interfere with nuclear receptors (NRs), key regulators of physiological processes. Despite the evident risks, the majority of existing res...

A deep learning feature extraction-based hybrid approach for detecting pediatric pneumonia in chest X-ray images.

Physical and engineering sciences in medicine
Pneumonia is a disease caused by bacteria, viruses, and fungi that settle in the alveolar sacs of the lungs and can lead to serious health complications in humans. Early detection of pneumonia is necessary for early treatment to manage and cure the d...

Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Recently, deep learning (DL) algorithms showed to be promising in predicting outcomes such as distant metastasis-free survival (DMFS) and overall survival (OS) using pre-treatment imaging in head and neck cancer. Gross Tumor...

SLAPP: Subgraph-level attention-based performance prediction for deep learning models.

Neural networks : the official journal of the International Neural Network Society
The intricacy of the Deep Learning (DL) landscape, brimming with a variety of models, applications, and platforms, poses considerable challenges for the optimal design, optimization, or selection of suitable DL models. One promising avenue to address...

Performance Analysis in Children of Traditional and Deep Learning CT Lung Nodule Computer-Aided Detection Systems Trained on Adults.

AJR. American journal of roentgenology
Although primary lung cancer is rare in children, chest CT is commonly performed to assess for lung metastases in children with cancer. Lung nodule computer-aided detection (CAD) systems have been designed and studied primarily using adult training ...

Operational greenhouse-gas emissions of deep learning in digital pathology: a modelling study.

The Lancet. Digital health
BACKGROUND: Deep learning is a promising way to improve health care. Image-processing medical disciplines, such as pathology, are expected to be transformed by deep learning. The first clinically applicable deep-learning diagnostic support tools are ...

Deep learning based automatic internal gross target volume delineation from 4D-CT of hepatocellular carcinoma patients.

Journal of applied clinical medical physics
BACKGROUND: The location and morphology of the liver are significantly affected by respiratory motion. Therefore, delineating the gross target volume (GTV) based on 4D medical images is more accurate than regular 3D-CT with contrast. However, the 4D ...

Deep learning reconstruction for improving the visualization of acute brain infarct on computed tomography.

Neuroradiology
PURPOSE: This study aimed to investigate the impact of deep learning reconstruction (DLR) on acute infarct depiction compared with hybrid iterative reconstruction (Hybrid IR).

Large-scale spatiotemporal deep learning predicting urban residential indoor PM concentration.

Environment international
Indoor PM pollution is one of the leading causes of death and disease worldwide. As monitoring indoor PM concentrations on a large scale is challenging, it is urgent to assess population-level exposure and related health risks to develop an easy-to-u...

Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction.

Science advances
Chemical shift assignment is vital for nuclear magnetic resonance (NMR)-based studies of protein structures, dynamics, and interactions, providing crucial atomic-level insight. However, obtaining chemical shift assignments is labor intensive and requ...