AIMC Topic: Neural Networks, Computer

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Evaluation of the dataset quality in gamma passing rate predictions using machine learning methods.

The British journal of radiology
OBJECTIVE: Gamma passing rate (GPR) predictions using machine learning methods have been explored for treatment verification of radiotherapy plans. However, these methods presented datasets with unbalanced number of plans having different treatment c...

Comparative study of lipid nanoparticle-based mRNA vaccine bioprocess with machine learning and combinatorial artificial neural network-design of experiment approach.

International journal of pharmaceutics
To develop a combinatorial artificial-neural-network design-of-experiment (ANN-DOE) model, the effect of ionizable lipid, an ionizable lipid-to-cholesterol ratio, N/P ratio, flow rate ratio (FRR), and total flow rate (TFR) on the outcome responses of...

Listen to the Brain-Auditory Sound Source Localization in Neuromorphic Computing Architectures.

Sensors (Basel, Switzerland)
Conventional processing of sensory input often relies on uniform sampling leading to redundant information and unnecessary resource consumption throughout the entire processing pipeline. Neuromorphic computing challenges these conventions by mimickin...

Collimation border with U-Net segmentation on chest radiographs compared to radiologists.

Radiography (London, England : 1995)
INTRODUCTION: Chest Radiography (CXR) is a common radiographic procedure. Radiation exposure to patients should be kept as low as reasonably achievable (ALARA), and monitored continuously as part of quality assurance (QA) programs. One of the most ef...

Coherent noise enables probabilistic sequence replay in spiking neuronal networks.

PLoS computational biology
Animals rely on different decision strategies when faced with ambiguous or uncertain cues. Depending on the context, decisions may be biased towards events that were most frequently experienced in the past, or be more explorative. A particular type o...

Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning.

IEEE transactions on medical imaging
Well-annotated medical datasets enable deep neural networks (DNNs) to gain strong power in extracting lesion-related features. Building such large and well-designed medical datasets is costly due to the need for high-level expertise. Model pre-traini...

Unsupervised Cryo-EM Images Denoising and Clustering Based on Deep Convolutional Autoencoder and K-Means+.

IEEE transactions on medical imaging
Cryo-electron microscopy (cryo-EM) is a widely used structural determination technique. Because of the extremely low signal-to-noise ratio (SNR) of images captured by cryo-EM, clustering single-particle cryo-EM images with high accuracy is challengin...

Deep Low-Shot Learning for Biological Image Classification and Visualization From Limited Training Samples.

IEEE transactions on neural networks and learning systems
Predictive modeling is useful but very challenging in biological image analysis due to the high cost of obtaining and labeling training data. For example, in the study of gene interaction and regulation in Drosophila embryogenesis, the analysis is mo...

Variable Binding for Sparse Distributed Representations: Theory and Applications.

IEEE transactions on neural networks and learning systems
Variable binding is a cornerstone of symbolic reasoning and cognition. But how binding can be implemented in connectionist models has puzzled neuroscientists, cognitive psychologists, and neural network researchers for many decades. One type of conne...

Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations.

Sensors (Basel, Switzerland)
Genome-wide association studies have proven their ability to improve human health outcomes by identifying genotypes associated with phenotypes. Various works have attempted to predict the risk of diseases for individuals based on genotype data. This ...