AIMC Topic: Neural Networks, Computer

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PCA driven mixed filter pruning for efficient convNets.

PloS one
Deployment of the deep neural networks (DNNs) on resource-constrained devices is a challenging task due to their limited memory and computational power. In most cases, the pruning techniques do not prune the DNNs to full extent and redundancy still e...

PCAT-UNet: UNet-like network fused convolution and transformer for retinal vessel segmentation.

PloS one
The accurate segmentation of retinal vessels images can not only be used to evaluate and monitor various ophthalmic diseases, but also timely reflect systemic diseases such as diabetes and blood diseases. Therefore, the study on segmentation of retin...

Evaluation of deep convolutional neural networks for in situ hybridization gene expression image representation.

PloS one
High resolution in situ hybridization (ISH) images of the brain capture spatial gene expression at cellular resolution. These spatial profiles are key to understanding brain organization at the molecular level. Previously, manual qualitative scoring ...

Assessment of medical waste generation, associated environmental impact, and management issues after the outbreak of COVID-19: A case study of the Hubei Province in China.

PloS one
COVID-19 greatly challenges the human health sector, and has resulted in a large amount of medical waste that poses various potential threats to the environment. In this study, we compiled relevant data released by official agencies and the media, an...

Multiple Protein Subcellular Locations Prediction Based on Deep Convolutional Neural Networks with Self-Attention Mechanism.

Interdisciplinary sciences, computational life sciences
As an important research field in bioinformatics, protein subcellular location prediction is critical to reveal the protein functions and provide insightful information for disease diagnosis and drug development. Predicting protein subcellular locati...

End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: To develop and validate a deep learning system for diabetic retinopathy (DR) grading based on fundus fluorescein angiography (FFA) images.

Weakly supervised segmentation with cross-modality equivariant constraints.

Medical image analysis
Weakly supervised learning has emerged as an appealing alternative to alleviate the need for large labeled datasets in semantic segmentation. Most current approaches exploit class activation maps (CAMs), which can be generated from image-level annota...

Performance change with the number of training data: A case study on the binary classification of COVID-19 chest X-ray by using convolutional neural networks.

Computers in biology and medicine
One of the features of artificial intelligence/machine learning-based medical devices resides in their ability to learn from real-world data. However, obtaining a large number of training data in the early phase is difficult, and the device performan...

Data Loss Reconstruction Method for a Bridge Weigh-in-Motion System Using Generative Adversarial Networks.

Sensors (Basel, Switzerland)
In the application of a bridge weigh-in-motion (WIM) system, the collected data may be temporarily or permanently lost due to sensor failure or system transmission failure. The high data loss rate weakens the distribution characteristics of the colle...

Rapid Quantitative Analysis of IR Absorption Spectra for Trace Gas Detection by Artificial Neural Networks Trained with Synthetic Data.

Sensors (Basel, Switzerland)
Infrared absorption spectroscopy is a widely used tool to quantify and monitor compositions of gases. The concentration information is often retrieved by fitting absorption profiles to the acquired spectra, utilizing spectroscopic databases. In compl...