AIMC Topic: Brain

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The contribution of object identity and configuration to scene representation in convolutional neural networks.

PloS one
Scene perception involves extracting the identities of the objects comprising a scene in conjunction with their configuration (the spatial layout of the objects in the scene). How object identity and configuration information is weighted during scene...

Ultralow Power Wearable Organic Ferroelectric Device for Optoelectronic Neuromorphic Computing.

Nano letters
In order to imitate brain-inspired biological information processing systems, various neuromorphic computing devices have been proposed, most of which were prepared on rigid substrates and have energy consumption levels several orders of magnitude hi...

Predicting In Vivo Compound Brain Penetration Using Multi-task Graph Neural Networks.

Journal of chemical information and modeling
Assessing whether compounds penetrate the brain can become critical in drug discovery, either to prevent adverse events or to reach the biological target. Generally, pre-clinical in vivo studies measuring the ratio of brain and blood concentrations (...

Prediction of Motor Outcome of Stroke Patients Using a Deep Learning Algorithm with Brain MRI as Input Data.

European neurology
BACKGROUND: Deep learning techniques can outperform traditional machine learning techniques and learn from unstructured and perceptual data, such as images and languages. We evaluated whether a convolutional neural network (CNN) model using whole axi...

Simultaneously optimizing sampling pattern for joint acceleration of multi-contrast MRI using model-based deep learning.

Medical physics
BACKGROUND: Acceleration of MR imaging (MRI) is a popular research area, and usage of deep learning for acceleration has become highly widespread in the MR community. Joint acceleration of multiple-acquisition MRI was proven to be effective over a si...

Face identity coding in the deep neural network and primate brain.

Communications biology
A central challenge in face perception research is to understand how neurons encode face identities. This challenge has not been met largely due to the lack of simultaneous access to the entire face processing neural network and the lack of a compreh...

A Novel Approach to Predict Brain Cancerous Tumor Using Transfer Learning.

Computational and mathematical methods in medicine
As the most prevalent and deadly malignancy, brain tumors have a dismal survival rate when they are at their most hazardous. Using mostly traditional medical image processing methods, segmenting and classifying brain malignant tumors is a challenging...

From YouTube to the brain: Transfer learning can improve brain-imaging predictions with deep learning.

Neural networks : the official journal of the International Neural Network Society
Deep learning has recently achieved best-in-class performance in several fields, including biomedical domains such as X-ray images. Yet, data scarcity poses a strict limit on training successful deep learning systems in many, if not most, biomedical ...

Deep learning-based quantitative susceptibility mapping (QSM) in the presence of fat using synthetically generated multi-echo phase training data.

Magnetic resonance in medicine
PURPOSE: To enable a fast and automatic deep learning-based QSM reconstruction of tissues with diverse chemical shifts, relevant to most regions outside the brain.

Systematic perturbation of an artificial neural network: A step towards quantifying causal contributions in the brain.

PLoS computational biology
Lesion inference analysis is a fundamental approach for characterizing the causal contributions of neural elements to brain function. This approach has gained new prominence through the arrival of modern perturbation techniques with unprecedented lev...