AIMC Topic: Mice

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Development of deep learning models for microglia analyses in brain tissue using DeePathology™ STUDIO.

Journal of neuroscience methods
BACKGROUND: Interest in artificial intelligence-driven analysis of medical images has seen a steep increase in recent years. Thus, our paper aims to promote and facilitate the use of this state-of-the-art technology to fellow researchers and clinicia...

Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders.

PLoS computational biology
Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for comput...

Peak learning of mass spectrometry imaging data using artificial neural networks.

Nature communications
Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving, biomarker discovery, metabolomics research, pharmaceutical applications and clinical diagnosis. Despite many solutions being developed, the large data size ...

Cross-species behavior analysis with attention-based domain-adversarial deep neural networks.

Nature communications
Since the variables inherent to various diseases cannot be controlled directly in humans, behavioral dysfunctions have been examined in model organisms, leading to better understanding their underlying mechanisms. However, because the spatial and tem...

Precise Control of Customized Macrophage Cell Robot for Targeted Therapy of Solid Tumors with Minimal Invasion.

Small (Weinheim an der Bergstrasse, Germany)
Injecting micro/nanorobots into the body to kill tumors is one of the ultimate ambitions for medical nanotechnology. However, injecting current micro/nanorobots based on 3D-printed biocompatible materials directly into blood vessels for targeted ther...

Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer's disease neuropathologies.

Nature communications
Deep neural networks (DNNs) capture complex relationships among variables, however, because they require copious samples, their potential has yet to be fully tapped for understanding relationships between gene expression and human phenotypes. Here we...

Markerless analysis of hindlimb kinematics in spinal cord-injured mice through deep learning.

Neuroscience research
Rodent models are commonly used to understand the underlying mechanisms of spinal cord injury (SCI). Kinematic analysis, an important technique to measure dysfunction of locomotion after SCI, is generally based on the capture of physical markers plac...

Deep learning models for benign and malign ocular tumor growth estimation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Relatively abundant availability of medical imaging data has provided significant support in the development and testing of Neural Network based image processing methods. Clinicians often face issues in selecting suitable image processing algorithm f...

NeuriteNet: A convolutional neural network for assessing morphological parameters of neurite growth.

Journal of neuroscience methods
BACKGROUND: During development or regeneration, neurons extend processes (i.e., neurites) via mechanisms that can be readily analyzed in culture. However, defining the impact of a drug or genetic manipulation on such mechanisms can be challenging due...

DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels.

eLife
Videos of animal behavior are used to quantify researcher-defined behaviors of interest to study neural function, gene mutations, and pharmacological therapies. Behaviors of interest are often scored manually, which is time-consuming, limited to few ...