AIMC Topic: Neural Networks, Computer

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Fine-tuned deep learning models for early detection and classification of kidney conditions in CT imaging.

Scientific reports
The kidney plays a vital role in maintaining homeostasis, but lifestyle factors and diseases can lead to kidney failures. Early detection of kidney disease is crucial for effective intervention, often challenging due to unnoticeable symptoms in the i...

Diffusion MRI GAN synthesizing fibre orientation distribution data using generative adversarial networks.

Communications biology
Machine learning may enhance clinical data analysis but requires large amounts of training data, which are scarce for rare pathologies. While generative neural network models can create realistic synthetic data such as 3D MRI volumes and, thus, augme...

A Framework for Parameter Estimation and Uncertainty Quantification in Systems Biology Using Quantile Regression and Physics-Informed Neural Networks.

Bulletin of mathematical biology
A framework for parameter estimation and uncertainty quantification is crucial for understanding the mechanisms of biological interactions within complex systems and exploring their dynamic behaviors beyond what can be experimentally observed. Despit...

A hybrid long short-term memory-convolutional neural network multi-stream deep learning model with Convolutional Block Attention Module incorporated for monkeypox detection.

Science progress
BackgroundMonkeypox (mpox) is a zoonotic infectious disease caused by the mpox virus and characterized by painful body lesions, fever, headaches, and exhaustion. Since the report of the first human case of mpox in Africa, there have been multiple out...

Enhancing target speaker extraction with Hierarchical Speaker Representation Learning.

Neural networks : the official journal of the International Neural Network Society
Target speaker extraction aims to obtain the speech of the specific speaker from a mixture of multiple voices. The conventional approach exploits the target speaker embeddings from a pre-recorded speech segment as auxiliary information, providing pri...

Using samples with label noise for robust continual learning.

Neural networks : the official journal of the International Neural Network Society
Recent studies have shown that effectively leveraging samples with label noise can enhance model robustness by uncovering more reliable feature patterns. While existing methods, such as label correction methods and loss correction techniques, have de...

Optimal cybersecurity framework for smart water system: Detection, localization and severity assessment.

Water research
The digital transformation of water distribution systems has streamlined monitoring and control through the integration of smart devices such as pressure sensors, smart meters, and level switches, all communicating with supervisory control and data a...

Adaptive bigraph-based multi-view unsupervised dimensionality reduction.

Neural networks : the official journal of the International Neural Network Society
As a crucial machine learning technology, graph-based multi-view unsupervised dimensionality reduction aims to learn compact low-dimensional representations for unlabeled multi-view data using graph structures. However, it faces several challenges, i...

LA-ResUNet: Attention-based network for longitudinal liver tumor segmentation from CT images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Longitudinal liver tumor segmentation plays a fundamental role in studying and monitoring the progression of associated diseases. The correlation and differences between longitudinal data can further improve segmentation performance, which are inevit...

scCorrect: Cross-modality label transfer from scRNA-seq to scATAC-seq using domain adaptation.

Analytical biochemistry
Cell type annotation in single-cell chromatin accessibility sequencing (scATAC-seq) is crucial for enabling researchers to identify subpopulations of cells associated with specific diseases, elucidate gene regulatory networks, and discover markers in...