AIMC Topic: Neural Networks, Computer

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Early Detection of Hypotension Using a Multivariate Machine Learning Approach.

Military medicine
INTRODUCTION: The ability to accurately detect hypotension in trauma patients at the earliest possible time is important in improving trauma outcomes. The earlier an accurate detection can be made, the more time is available to take corrective action...

Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space.

Cell reports
It is well known that the development of drug resistance in cancer cells can lead to changes in cell morphology. Here, we describe the use of deep neural networks to analyze this relationship, demonstrating that complex cell morphologies can encode s...

Unsupervised neural network models of the ventral visual stream.

Proceedings of the National Academy of Sciences of the United States of America
Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model of the development of the ventral stream...

Passive imaging at 250 GHz for detection of face presentation attacks.

Optics express
Face presentation attacks are becoming more efficient since new 3D facial masks are used. Passive terahertz imaging offers specific physical properties that may improve presentation attack detection capabilities. The non-zero transmission capability ...

Visualizing population structure with variational autoencoders.

G3 (Bethesda, Md.)
Dimensionality reduction is a common tool for visualization and inference of population structure from genotypes, but popular methods either return too many dimensions for easy plotting (PCA) or fail to preserve global geometry (t-SNE and UMAP). Here...

Anticancer drug synergy prediction in understudied tissues using transfer learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Drug combination screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that the accumulated number of observations in in-vitro drug responses varies...

Deep sparse transfer learning for remote smart tongue diagnosis.

Mathematical biosciences and engineering : MBE
People are exploring new ideas based on artificial intelligent infrastructures for immediate processing, in which the main obstacles of widely-deploying deep methods are the huge volume of neural network and the lack of training data. To meet the hig...

Deep learning for in vivo near-infrared imaging.

Proceedings of the National Academy of Sciences of the United States of America
Detecting fluorescence in the second near-infrared window (NIR-II) up to ∼1,700 nm has emerged as a novel in vivo imaging modality with high spatial and temporal resolution through millimeter tissue depths. Imaging in the NIR-IIb window (1,500-1,700 ...

DS-GCNs: Connectome Classification using Dynamic Spectral Graph Convolution Networks with Assistant Task Training.

Cerebral cortex (New York, N.Y. : 1991)
Functional connectivity (FC) matrices measure the regional interactions in the brain and have been widely used in neurological brain disease classification. A brain network, also named as connectome, could form a graph structure naturally, the nodes ...