AIMC Topic: Deep Learning

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Monitoring Amyloidogenesis with a 3D Deep-Learning-Guided Biolaser Imaging Array.

Nano letters
Amyloidogenesis is a critical hallmark for many neurodegenerative diseases and drug screening; however, identifying intermediate states of protein aggregates at an earlier stage remains challenging. Herein, we developed a peptide-encapsulated droplet...

Deep Learning-Based Segmentation of Cryo-Electron Tomograms.

Journal of visualized experiments : JoVE
Cryo-electron tomography (cryo-ET) allows researchers to image cells in their native, hydrated state at the highest resolution currently possible. The technique has several limitations, however, that make analyzing the data it generates time-intensiv...

A New Hybrid Neural Network Deep Learning Method for Protein-Ligand Binding Affinity Prediction and De Novo Drug Design.

International journal of molecular sciences
Accurately predicting ligand binding affinity in a virtual screening campaign is still challenging. Here, we developed hybrid neural network (HNN) machine deep learning methods, HNN-denovo and HNN-affinity, by combining the 3D-CNN (convolutional neur...

Novel deep learning hybrid models (CNN-GRU and DLDL-RF) for the susceptibility classification of dust sources in the Middle East: a global source.

Scientific reports
Dust storms have many negative consequences, and affect all kinds of ecosystems, as well as climate and weather conditions. Therefore, classification of dust storm sources into different susceptibility categories can help us mitigate its negative eff...

A fully automated deep learning pipeline for micro-CT-imaging-based densitometry of lung fibrosis murine models.

Respiratory research
Idiopathic pulmonary fibrosis, the archetype of pulmonary fibrosis (PF), is a chronic lung disease of a poor prognosis, characterized by progressively worsening of lung function. Although histology is still the gold standard for PF assessment in prec...

A deep learning approach for automated diagnosis of pulmonary embolism on computed tomographic pulmonary angiography.

BMC medical imaging
BACKGROUND: Computed tomographic pulmonary angiography (CTPA) is the diagnostic standard for confirming pulmonary embolism (PE). Since PE is a life-threatening condition, early diagnosis and treatment are critical to avoid PE-associated morbidity and...

Shuffle-ResNet: Deep learning for predicting LGG IDH1 mutation from multicenter anatomical MRI sequences.

Biomedical physics & engineering express
The world health organization recommended to incorporate gene information such as isocitrate dehydrogenase 1 (IDH1) mutation status to improve prognosis, diagnosis, and treatment of the central nervous system tumors. We proposed our Shuffle Residual ...

Measuring and modelling perceptions of the built environment for epidemiological research using crowd-sourcing and image-based deep learning models.

Journal of exposure science & environmental epidemiology
BACKGROUND: Perceptions of the built environment, such as nature quality, beauty, relaxation, and safety, may be key factors linking the built environment to human health. However, few studies have examined these types of perceptions due to the diffi...

DeepProjection: specific and robust projection of curved 2D tissue sheets from 3D microscopy using deep learning.

Development (Cambridge, England)
The efficient extraction of image data from curved tissue sheets embedded in volumetric imaging data remains a serious and unsolved problem in quantitative studies of embryogenesis. Here, we present DeepProjection (DP), a trainable projection algorit...

DeepCAN: A Modular Deep Learning System for Automated Cell Counting and Viability Analysis.

IEEE journal of biomedical and health informatics
Precise and quick monitoring of key cytometric features such as cell count, size, morphology, and DNA content is crucial in life science applications. Traditionally, image cytometry relies on visual inspection of hemocytometers. This approach is erro...