AIMC Topic: Deep Learning

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Structure-Coherent Deep Feature Learning for Robust Face Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
In this paper, we propose a structure-coherent deep feature learning method for face alignment. Unlike most existing face alignment methods which overlook the facial structure cues, we explicitly exploit the relation among facial landmarks to make th...

k-Space-based coil combination via geometric deep learning for reconstruction of non-Cartesian MRSI data.

Magnetic resonance in medicine
PURPOSE: State-of-the-art whole-brain MRSI with spatial-spectral encoding and multichannel acquisition generates huge amounts of data, which must be efficiently processed to stay within reasonable reconstruction times. Although coil combination signi...

Deep learning for tracing esophageal motility function over time.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Esophageal high-resolution manometry (HRM) is widely performed to evaluate the representation of manometric features in patients for diagnosing normal esophageal motility and motility disorders. Clinicians commonly assess es...

Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma.

Cell systems
Deep learning has emerged as the technique of choice for identifying hidden patterns in cell imaging data but is often criticized as "black box." Here, we employ a generative neural network in combination with supervised machine learning to classify ...

In silico drug repositioning using deep learning and comprehensive similarity measures.

BMC bioinformatics
BACKGROUND: Drug repositioning, meanings finding new uses for existing drugs, which can accelerate the processing of new drugs research and development. Various computational methods have been presented to predict novel drug-disease associations for ...

Current directions in combining simulation-based macromolecular modeling approaches with deep learning.

Expert opinion on drug discovery
: Structure-guided drug discovery relies on accurate computational methods for modeling macromolecules. Simulations provide means of predicting macromolecular folds, of discovering function from structure, and of designing macromolecules to serve as ...

Deep Learning of Binary Solution Phase Behavior of Polystyrene.

ACS macro letters
Predicting binary solution phase behavior of polymers has remained a challenge since the early theory of Flory-Huggins, hindering the processing, synthesis, and design of polymeric materials. Herein, we take a complementary data-driven approach by bu...

Synthetic CT generation from CBCT images via unsupervised deep learning.

Physics in medicine and biology
Adaptive-radiation-therapy (ART) is applied to account for anatomical variations observed over the treatment course. Daily or weekly cone-beam computed tomography (CBCT) is commonly used in clinic for patient positioning, but CBCT's inaccuracy in Hou...