AIMC Topic: Neural Networks, Computer

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Density Prediction Models for Energetic Compounds Merely Using Molecular Topology.

Journal of chemical information and modeling
Newly developed high-throughput methods for property predictions make the process of materials design faster and more efficient. Density is an important physical property for energetic compounds to assess detonation velocity and detonation pressure, ...

RPLS-Net: pulmonary lobe segmentation based on 3D fully convolutional networks and multi-task learning.

International journal of computer assisted radiology and surgery
PURPOSE: The robust and automatic segmentation of the pulmonary lobe is vital to surgical planning and regional image analysis of pulmonary related diseases in real-time Computer Aided Diagnosis systems. While a number of studies have examined this i...

Flexible modulation of sequence generation in the entorhinal-hippocampal system.

Nature neuroscience
Exploration, consolidation and planning depend on the generation of sequential state representations. However, these algorithms require disparate forms of sampling dynamics for optimal performance. We theorize how the brain should adapt internally ge...

Accelerated multicontrast reconstruction for synthetic MRI using joint parallel imaging and variable splitting networks.

Medical physics
PURPOSE: Synthetic magnetic resonance imaging (MRI) requires the acquisition of multicontrast images to estimate quantitative parameter maps, such as T , T , and proton density (PD). The study aims to develop a multicontrast reconstruction method bas...

Risks of feature leakage and sample size dependencies in deep feature extraction for breast mass classification.

Medical physics
PURPOSE: Transfer learning is commonly used in deep learning for medical imaging to alleviate the problem of limited available data. In this work, we studied the risk of feature leakage and its dependence on sample size when using pretrained deep con...

Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks.

Sensors (Basel, Switzerland)
The accuracy in diagnosing prostate cancer (PCa) has increased with the development of multiparametric magnetic resonance imaging (mpMRI). Biparametric magnetic resonance imaging (bpMRI) was found to have a diagnostic accuracy comparable to mpMRI in ...

Tens of images can suffice to train neural networks for malignant leukocyte detection.

Scientific reports
Convolutional neural networks (CNNs) excel as powerful tools for biomedical image classification. It is commonly assumed that training CNNs requires large amounts of annotated data. This is a bottleneck in many medical applications where annotation r...

Impact of image compression on deep learning-based mammogram classification.

Scientific reports
Image compression is used in several clinical organizations to help address the overhead associated with medical imaging. These methods reduce file size by using a compact representation of the original image. This study aimed to analyze the impact o...

Performance improvement for a 2D convolutional neural network by using SSC encoding on protein-protein interaction tasks.

BMC bioinformatics
BACKGROUND: The interactions of proteins are determined by their sequences and affect the regulation of the cell cycle, signal transduction and metabolism, which is of extraordinary significance to modern proteomics research. Despite advances in expe...

Artificial intelligence for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging.

EBioMedicine
BACKGROUND: artificial intelligence (AI) for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging (WSI) is lacking. We aim to establish an AI chronic rhinosinusitis evaluation platform 2.0 (AICEP 2.0) to obtain the proportion of infl...