AIMC Topic: Deep Learning

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Multiparameter optimisation of a magneto-optical trap using deep learning.

Nature communications
Machine learning based on artificial neural networks has emerged as an efficient means to develop empirical models of complex systems. Cold atomic ensembles have become commonplace in laboratories around the world, however, many-body interactions giv...

High-Fidelity Monocular Face Reconstruction Based on an Unsupervised Model-Based Face Autoencoder.

IEEE transactions on pattern analysis and machine intelligence
In this work, we propose a novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image. To this end, we combine a convolutional encoder network...

Machine Meets Biology: a Primer on Artificial Intelligence in Cardiology and Cardiac Imaging.

Current cardiology reports
PURPOSE OF REVIEW: An understanding of the basics concepts of deep learning can be helpful in not only understanding the potential applications of this technique but also in critically reviewing literature in which neural networks are utilized for an...

Diagnostic accuracy of content-based dermatoscopic image retrieval with deep classification features.

The British journal of dermatology
BACKGROUND: Automated classification of medical images through neural networks can reach high accuracy rates but lacks interpretability.

Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT.

IEEE transactions on medical imaging
The accurate identification of malignant lung nodules on chest CT is critical for the early detection of lung cancer, which also offers patients the best chance of cure. Deep learning methods have recently been successfully introduced to computer vis...

End-to-End syndrome differentiation of Yin deficiency and Yang deficiency in traditional Chinese medicine.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Yin and Yang, two concepts adapted from classical Chinese philosophy, play a diagnostic role in Traditional Chinese Medicine (TCM). The Yin and Yang in harmonious balance indicate health, whereas imbalances to either side in...

Regularization of deep neural networks with spectral dropout.

Neural networks : the official journal of the International Neural Network Society
The big breakthrough on the ImageNet challenge in 2012 was partially due to the 'Dropout' technique used to avoid overfitting. Here, we introduce a new approach called 'Spectral Dropout' to improve the generalization ability of deep neural networks. ...

Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation.

Radiology
Purpose To develop a deep learning (DL) algorithm to assess mammographic breast density. Materials and Methods In this retrospective study, a deep convolutional neural network was trained to assess Breast Imaging Reporting and Data System (BI-RADS) b...

Using neural attention networks to detect adverse medical events from electronic health records.

Journal of biomedical informatics
The detection of Adverse Medical Events (AMEs) plays an important role in disease management in ensuring efficient treatment delivery and quality improvement of health services. Recently, with the rapid development of hospital information systems, a ...