AIMC Topic: Algorithms

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DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping.

NeuroImage
Quantitative susceptibility mapping (QSM) is based on magnetic resonance imaging (MRI) phase measurements and has gained broad interest because it yields relevant information on biological tissue properties, predominantly myelin, iron and calcium in ...

Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination.

European journal of pediatrics
Lung auscultation is an important part of a physical examination. However, its biggest drawback is its subjectivity. The results depend on the experience and ability of the doctor to perceive and distinguish pathologies in sounds heard via a stethosc...

Automatic localization and identification of mitochondria in cellular electron cryo-tomography using faster-RCNN.

BMC bioinformatics
BACKGROUND: Cryo-electron tomography (cryo-ET) enables the 3D visualization of cellular organization in near-native state which plays important roles in the field of structural cell biology. However, due to the low signal-to-noise ratio (SNR), large ...

Information Theoretic Feature Transformation Learning for Brain Interfaces.

IEEE transactions on bio-medical engineering
OBJECTIVE: A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics. In the light of broad evidence demonstrating the potential s...

iMotor-CNN: Identifying molecular functions of cytoskeleton motor proteins using 2D convolutional neural network via Chou's 5-step rule.

Analytical biochemistry
Motor proteins are the driving force behind muscle contraction and are responsible for the active transportation of most proteins and vesicles in the cytoplasm. There are three superfamilies of cytoskeletal motor proteins with various molecular funct...

Estimating Multiscale Direct Causality Graphs in Neural Spike-Field Networks.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Neural representations span various spatiotemporal scales of brain activity, from the spiking activity of single neurons to field activity measuring large-scale networks. The simultaneous analyses of spikes and fields to uncover causal interactions i...

Artificial intelligence to diagnose meniscus tears on MRI.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to build and evaluate a high-performance algorithm to detect and characterize the presence of a meniscus tear on magnetic resonance imaging examination (MRI) of the knee.

Selene: a PyTorch-based deep learning library for sequence data.

Nature methods
To enable the application of deep learning in biology, we present Selene (https://selene.flatironinstitute.org/), a PyTorch-based deep learning library for fast and easy development, training, and application of deep learning model architectures for ...

Evolutionary Spiking Neural Networks for Solving Supervised Classification Problems.

Computational intelligence and neuroscience
This paper presents a grammatical evolution (GE)-based methodology to automatically design third generation artificial neural networks (ANNs), also known as spiking neural networks (SNNs), for solving supervised classification problems. The proposal ...

Intelligent Labeling Based on Fisher Information for Medical Image Segmentation Using Deep Learning.

IEEE transactions on medical imaging
Deep convolutional neural networks (CNN) have recently achieved superior performance at the task of medical image segmentation compared to classic models. However, training a generalizable CNN requires a large amount of training data, which is diffic...