AIMC Topic: Deep Learning

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Deep reinforcement learning for de novo drug design.

Science advances
We have devised and implemented a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). On the basis of deep and reinforcement learning (RL) approaches, ...

Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning Approach.

IEEE transactions on pattern analysis and machine intelligence
Panoramic video provides immersive and interactive experience by enabling humans to control the field of view (FoV) through head movement (HM). Thus, HM plays a key role in modeling human attention on panoramic video. This paper establishes a databas...

3D-Aided Dual-Agent GANs for Unconstrained Face Recognition.

IEEE transactions on pattern analysis and machine intelligence
Synthesizing realistic profile faces is beneficial for more efficiently training deep pose-invariant models for large-scale unconstrained face recognition, by augmenting the number of samples with extreme poses and avoiding costly annotation work. Ho...

Extracting psychiatric stressors for suicide from social media using deep learning.

BMC medical informatics and decision making
BACKGROUND: Suicide has been one of the leading causes of deaths in the United States. One major cause of suicide is psychiatric stressors. The detection of psychiatric stressors in an at risk population will facilitate the early prevention of suicid...

Chemical-induced disease extraction via recurrent piecewise convolutional neural networks.

BMC medical informatics and decision making
BACKGROUND: Extracting relationships between chemicals and diseases from unstructured literature have attracted plenty of attention since the relationships are very useful for a large number of biomedical applications such as drug repositioning and p...

Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge.

BMC medical informatics and decision making
BACKGROUND: The emergence of the deep convolutional neural network (CNN) greatly improves the quality of computer-aided supporting systems. However, due to the challenges of generating reliable and timely results, clinical adoption of computer-aided ...

A machine-learning framework for automatic reference-free quality assessment in MRI.

Magnetic resonance imaging
Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in order to derive a meaningful diagnosis. This is a manual process and th...

Deep neural models for extracting entities and relationships in the new RDD corpus relating disabilities and rare diseases.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: There is a huge amount of rare diseases, many of which have associated important disabilities. It is paramount to know in advance the evolution of the disease in order to limit and prevent the appearance of disabilities and ...

De novo profile generation based on sequence context specificity with the long short-term memory network.

BMC bioinformatics
BACKGROUND: Long short-term memory (LSTM) is one of the most attractive deep learning methods to learn time series or contexts of input data. Increasing studies, including biological sequence analyses in bioinformatics, utilize this architecture. Ami...

An effective teeth recognition method using label tree with cascade network structure.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In this article, we apply the deep learning technique to medical field for the teeth detection and classification of dental periapical radiographs, which is important for the medical curing and postmortem identification. We detect teeth in an input X...