AIMC Topic: Neural Networks, Computer

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Estimating myelin-water content from anatomical and diffusion images using spatially undersampled myelin-water imaging through machine learning.

NeuroImage
Myelin is vital for healthy neuronal development, and can therefore provide valuable information regarding neuronal maturation. Anatomical and diffusion weighted images (DWI) possess information related to the myelin content and the current study inv...

Pulmonary Ventilation Maps Generated with Free-breathing Proton MRI and a Deep Convolutional Neural Network.

Radiology
Background Hyperpolarized noble gas MRI helps measure lung ventilation, but clinical translation remains limited. Free-breathing proton MRI may help quantify lung function using existing MRI systems without contrast material and may assist in providi...

PsychoAge and SubjAge: development of deep markers of psychological and subjective age using artificial intelligence.

Aging
Aging clocks that accurately predict human age based on various biodata types are among the most important recent advances in biogerontology. Since 2016 multiple deep learning solutions have been created to interpret facial photos, omics data, and cl...

A machine learning toolkit for genetic engineering attribution to facilitate biosecurity.

Nature communications
The promise of biotechnology is tempered by its potential for accidental or deliberate misuse. Reliably identifying telltale signatures characteristic to different genetic designers, termed 'genetic engineering attribution', would deter misuse, yet i...

A generative spiking neural-network model of goal-directed behaviour and one-step planning.

PLoS computational biology
In mammals, goal-directed and planning processes support flexible behaviour used to face new situations that cannot be tackled through more efficient but rigid habitual behaviours. Within the Bayesian modelling approach of brain and behaviour, models...

Cell segmentation and tracking using CNN-based distance predictions and a graph-based matching strategy.

PloS one
The accurate segmentation and tracking of cells in microscopy image sequences is an important task in biomedical research, e.g., for studying the development of tissues, organs or entire organisms. However, the segmentation of touching cells in image...

Predicting MicroRNA Sequence Using CNN and LSTM Stacked in Seq2Seq Architecture.

IEEE/ACM transactions on computational biology and bioinformatics
CNN and LSTM have proven their ability in feature extraction and natural language processing, respectively. So, we tried to use their ability to process the language of RNAs, i.e., predicting sequence of microRNAs using the sequence of mRNA. The idea...

LSTM-Based End-to-End Framework for Biomedical Event Extraction.

IEEE/ACM transactions on computational biology and bioinformatics
Biomedical event extraction plays an important role in the extraction of biological information from large-scale scientific publications. However, most state-of-the-art systems separate this task into several steps, which leads to cascading errors. I...

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.

Nature methods
Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, t...

Strong inhibitory signaling underlies stable temporal dynamics and working memory in spiking neural networks.

Nature neuroscience
Cortical neurons process information on multiple timescales, and areas important for working memory (WM) contain neurons capable of integrating information over a long timescale. However, the underlying mechanisms for the emergence of neuronal timesc...