AIMC Topic: Neural Networks, Computer

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Segmentation Performance Comparison Considering Regional Characteristics in Chest X-ray Using Deep Learning.

Sensors (Basel, Switzerland)
Chest radiography is one of the most widely used diagnostic methods in hospitals, but it is difficult to read clearly because several human organ tissues and bones overlap. Therefore, various image processing and rib segmentation methods have been pr...

Process-Driven Modelling of Media Forensic Investigations-Considerations on the Example of DeepFake Detection.

Sensors (Basel, Switzerland)
Academic research in media forensics mainly focuses on methods for the detection of the traces or artefacts left by media manipulations in media objects. While the resulting detectors often achieve quite impressive detection performances, when tested...

Deduction learning for precise noninvasive measurements of blood glucose with a dozen rounds of data for model training.

Scientific reports
Personalized modeling has long been anticipated to approach precise noninvasive blood glucose measurements, but challenged by limited data for training personal model and its unavoidable outlier predictions. To overcome these long-standing problems, ...

Predicting the functional impact of KCNQ1 variants with artificial neural networks.

PLoS computational biology
Recent advances in experimental and computational protein structure determination have provided access to high-quality structures for most human proteins and mutants thereof. However, linking changes in structure in protein mutants to functional impa...

A Comprehensive Review of Recent Deep Learning Techniques for Human Activity Recognition.

Computational intelligence and neuroscience
Human action recognition is an important field in computer vision that has attracted remarkable attention from researchers. This survey aims to provide a comprehensive overview of recent human action recognition approaches based on deep learning usin...

Closing the Control Loop with Time-Variant Embedded Soft Sensors and Recurrent Neural Networks.

Soft robotics
Embedded soft sensors can significantly impact the design and control of soft-bodied robots. Although there have been considerable advances in technology behind these novel sensing materials, their application in real-world tasks, especially in close...

An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2.5 concentration in urban environment.

The Science of the total environment
This study proposes a new model for the spatiotemporal prediction of PM concentration at hourly and daily time intervals. It has been constructed on a combination of three-dimensional convolutional neural network and gated recurrent unit (3D CNN-GRU)...

Tri-view two-photon microscopic image registration and deblurring with convolutional neural networks.

Neural networks : the official journal of the International Neural Network Society
Two-photon fluorescence microscopy has enabled the three-dimensional (3D) neural imaging of deep cortical regions. While it can capture the detailed neural structures in the x-y image space, the image quality along the depth direction is lower becaus...

Vision Transformer for femur fracture classification.

Injury
INTRODUCTION: In recent years, the scientific community focused on developing Computer-Aided Diagnosis (CAD) tools that could improve clinicians' bone fractures diagnosis, primarily based on Convolutional Neural Networks (CNNs). However, the discerni...

A method to classify bone marrow cells with rejected option.

Biomedizinische Technik. Biomedical engineering
Bone marrow cell morphology has always been an important tool for the diagnosis of blood diseases. Still, it requires years of experience from a suitable person. Furthermore, the outcomes of their recognition are subjective and there is no objective ...