AIMC Topic: Deep Learning

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Automated Quality Assessment and Image Selection of Ultra-Widefield Fluorescein Angiography Images through Deep Learning.

Translational vision science & technology
PURPOSE: Numerous angiographic images with high variability in quality are obtained during each ultra-widefield fluorescein angiography (UWFA) acquisition session. This study evaluated the feasibility of an automated system for image quality classifi...

Group-based local adaptive deep multiple kernel learning with lp norm.

PloS one
The deep multiple kernel Learning (DMKL) method has attracted wide attention due to its better classification performance than shallow multiple kernel learning. However, the existing DMKL methods are hard to find suitable global model parameters to i...

Deep learning for automated glaucomatous optic neuropathy detection from ultra-widefield fundus images.

The British journal of ophthalmology
BACKGROUND/AIMS: To develop a deep learning system for automated glaucomatous optic neuropathy (GON) detection using ultra-widefield fundus (UWF) images.

Deep learning method for segmentation of rotator cuff muscles on MR images.

Skeletal radiology
OBJECTIVE: To develop and validate a deep convolutional neural network (CNN) method capable of (1) selecting a specific shoulder sagittal MR image (Y-view) and (2) automatically segmenting rotator cuff (RC) muscles on a Y-view. We hypothesized a CNN ...

Sanders classification of calcaneal fractures in CT images with deep learning and differential data augmentation techniques.

Injury
BACKGROUND: Classification of the type of calcaneal fracture on CT images is essential in driving treatment. However, human-based classification can be challenging due to anatomical complexities and CT image constraints. The use of computer-aided cla...

DNSS2: Improved ab initio protein secondary structure prediction using advanced deep learning architectures.

Proteins
Accurate prediction of protein secondary structure (alpha-helix, beta-strand and coil) is a crucial step for protein inter-residue contact prediction and ab initio tertiary structure prediction. In a previous study, we developed a deep belief network...

Analysis of the transferability and robustness of GANs evolved for Pareto set approximations.

Neural networks : the official journal of the International Neural Network Society
The generative adversarial network (GAN) is a good example of a strong-performing, neural network-based generative model, even though it does have some drawbacks of its own. Mode collapsing and the difficulty in finding the optimal network structure ...

Deep learning can generate traditional retinal fundus photographs using ultra-widefield images via generative adversarial networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Retinal imaging has two major modalities, traditional fundus photography (TFP) and ultra-widefield fundus photography (UWFP). This study demonstrates the feasibility of a state-of-the-art deep learning-based domain transfer ...

3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning.

Journal of chemical information and modeling
Generative models have been successfully used to synthesize completely novel images, text, music, and speech. As such, they present an exciting opportunity for the design of new materials for functional applications. So far, generative deep-learning ...

Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity.

Lab on a chip
The association of the intrinsic optical and biophysical properties of cells to homeostasis and pathogenesis has long been acknowledged. Defining these label-free cellular features obviates the need for costly and time-consuming labelling protocols t...