AIMC Topic: Neural Networks, Computer

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Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment.

Journal of chemical information and modeling
Graph neural networks are able to solve certain drug discovery tasks such as molecular property prediction and molecule generation. However, these models are considered "black-box" and "hard-to-debug". This study aimed to improve modeling transparen...

Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning.

Journal of chemical information and modeling
The development of efficient models for predicting specific properties through machine learning is of great importance for the innovation of chemistry and material science. However, predicting global electronic structure properties like Frontier mole...

Deep Learning for Automatic Segmentation of Hybrid Optoacoustic Ultrasound (OPUS) Images.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
The highly complementary information provided by multispectral optoacoustics and pulse-echo ultrasound have recently prompted development of hybrid imaging instruments bringing together the unique contrast advantages of both modalities. In the hybrid...

Establishment and clinical application value of an automatic diagnosis platform for rectal cancer T-staging based on a deep neural network.

Chinese medical journal
BACKGROUND: Colorectal cancer is harmful to the patient's life. The treatment of patients is determined by accurate preoperative staging. Magnetic resonance imaging (MRI) played an important role in the preoperative examination of patients with recta...

Predicting neurological outcome after out-of-hospital cardiac arrest with cumulative information; development and internal validation of an artificial neural network algorithm.

Critical care (London, England)
BACKGROUND: Prognostication of neurological outcome in patients who remain comatose after cardiac arrest resuscitation is complex. Clinical variables, as well as biomarkers of brain injury, cardiac injury, and systemic inflammation, all yield some pr...

A study on CNN image classification of EEG signals represented in 2D and 3D.

Journal of neural engineering
The novelty of this study consists of the exploration of multiple new approaches of data pre-processing of brainwave signals, wherein statistical features are extracted and then formatted as visual images based on the order in which dimensionality re...

COVID-19 in Iran: Forecasting Pandemic Using Deep Learning.

Computational and mathematical methods in medicine
COVID-19 has led to a pandemic, affecting almost all countries in a few months. In this work, we applied selected deep learning models including multilayer perceptron, random forest, and different versions of long short-term memory (LSTM), using thre...

Hybrid manifold-deep convolutional neural network for sleep staging.

Methods (San Diego, Calif.)
Analysis of electroencephalogram (EEG) is a crucial diagnostic criterion for many sleep disorders, of which sleep staging is an important component. Manual stage classification is a labor-intensive process and usually suffered from many subjective fa...

Searching Images for Consensus: Can AI Remove Observer Variability in Pathology?

The American journal of pathology
One of the major obstacles in reaching diagnostic consensus is observer variability. With the recent success of artificial intelligence, particularly the deep networks, the question emerges as to whether the fundamental challenge of diagnostic imagin...

Semisupervised adversarial neural networks for single-cell classification.

Genome research
Annotating cell identities is a common bottleneck in the analysis of single-cell genomics experiments. Here, we present scNym, a semisupervised, adversarial neural network that learns to transfer cell identity annotations from one experiment to anoth...