AIMC Topic: Neural Networks, Computer

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MAGIC: Manifold and Graph Integrative Convolutional Network for Low-Dose CT Reconstruction.

IEEE transactions on medical imaging
Low-dose computed tomography (LDCT) scans, which can effectively alleviate the radiation problem, will degrade the imaging quality. In this paper, we propose a novel LDCT reconstruction network that unrolls the iterative scheme and performs in both i...

Weak Human Preference Supervision for Deep Reinforcement Learning.

IEEE transactions on neural networks and learning systems
The current reward learning from human preferences could be used to resolve complex reinforcement learning (RL) tasks without access to a reward function by defining a single fixed preference between pairs of trajectory segments. However, the judgmen...

Deep Neural Network With Consistency Regularization of Multi-Output Channels for Improved Tumor Detection and Delineation.

IEEE transactions on medical imaging
Deep learning is becoming an indispensable tool for imaging applications, such as image segmentation, classification, and detection. In this work, we reformulate a standard deep learning problem into a new neural network architecture with multi-outpu...

Estimating Effective Connectivity by Recurrent Generative Adversarial Networks.

IEEE transactions on medical imaging
Estimating effective connectivity from functional magnetic resonance imaging (fMRI) time series data has become a very hot topic in neuroinformatics and brain informatics. However, it is hard for the current methods to accurately estimate the effecti...

Automatic Searching and Pruning of Deep Neural Networks for Medical Imaging Diagnostic.

IEEE transactions on neural networks and learning systems
The field of medical imaging diagnostic makes use of a modality of imaging tests, e.g., X-rays, ultrasounds, computed tomographies, and magnetic resonance imaging, to assist physicians with the diagnostic of patients' illnesses. Due to their state-of...

SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network.

Biosensors
Surface-Enhanced Raman Spectroscopy (SERS)-based biomolecule detection has been a challenge due to large variations in signal intensity, spectral profile, and nonlinearity. Recent advances in machine learning offer great opportunities to address thes...

CRNNTL: Convolutional Recurrent Neural Network and Transfer Learning for QSAR Modeling in Organic Drug and Material Discovery.

Molecules (Basel, Switzerland)
Molecular latent representations, derived from autoencoders (AEs), have been widely used for drug or material discovery over the past couple of years. In particular, a variety of machine learning methods based on latent representations have shown exc...

The Whale Optimization Algorithm Approach for Deep Neural Networks.

Sensors (Basel, Switzerland)
One of the biggest challenge in the field of deep learning is the parameter selection and optimization process. In recent years different algorithms have been proposed including bio-inspired solutions to solve this problem, however, there are many ch...

Neural Architecture Search for 1D CNNs-Different Approaches Tests and Measurements.

Sensors (Basel, Switzerland)
In the field of sensors, in areas such as industrial, clinical, or environment, it is common to find one dimensional (1D) formatted data (e.g., electrocardiogram, temperature, power consumption). A very promising technique for modelling this informat...

Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence.

Scientific reports
Since the 2019 novel coronavirus disease (COVID-19) outbreak in 2019 and the pandemic continues for more than one year, a vast amount of drug research has been conducted and few of them got FDA approval. Our objective is to prioritize repurposable dr...