AIMC Topic: Deep Learning

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Self-Supervised Learning for Feature Extraction from Glomerular Images and Disease Classification with Minimal Annotations.

Journal of the American Society of Nephrology : JASN
BACKGROUND: Deep learning has great potential in digital kidney pathology. However, its effectiveness depends heavily on the availability of extensively labeled datasets, which are often limited because of the specialized knowledge and time required ...

Context-aware feature reconstruction for class-incremental anomaly detection and localization.

Neural networks : the official journal of the International Neural Network Society
With the development of deep learning, the unsupervised visual anomaly detection and localization task has gained significant attention in both academia and industry, where only normal data are used for training. Existing methods for this task typica...

Acoustic leak localization for water distribution network through time-delay-based deep learning approach.

Water research
Water leakage within water distribution networks (WDNs) presents significant challenges, encompassing infrastructure damage, economic losses, and public health risks. Traditional methods for leak localization based on acoustic signals encounter inher...

Rapid identification of Radix Astragali by data fusion of laser-induced breakdown spectroscopy and Raman spectroscopy coupled with deep learning.

Talanta
The accurate identification of Radix Astragali holds significant scientific importance for evaluating the quality and medicinal efficacy of this herb. In this study, we introduced an efficient methodology, integrating laser induced breakdown spectros...

Analysis of nailfold capillaroscopy images with artificial intelligence: Data from literature and performance of machine learning and deep learning from images acquired in the SCLEROCAP study.

Microvascular research
OBJECTIVE: To evaluate the performance of machine learning and then deep learning to detect a systemic scleroderma (SSc) landscape from the same set of nailfold capillaroscopy (NC) images from the French prospective multicenter observational study SC...

Machine Learning, Deep Learning, Artificial Intelligence and Aesthetic Plastic Surgery: A Qualitative Systematic Review.

Aesthetic plastic surgery
PURPOSE: This systematic review aims to assess the use of machine learning, deep learning, and artificial intelligence in aesthetic plastic surgery.

Deep-learning and data-resampling: A novel approach to predict cyanobacterial alert levels in a reservoir.

Environmental research
The proliferation of harmful algal blooms results in adverse impacts on aquatic ecosystems and public health. Early warning system monitors algal bloom occurrences and provides management strategies for promptly addressing high-concentration algal bl...

Multi-label material and human risk factors recognition model for construction site safety management.

Journal of safety research
INTRODUCTION: Construction sites are prone to numerous safety risk factors, but safety managers have difficulty managing these risk factors for practical reasons. Moreover, manually identifying multiple risk factors visually is challenging. Therefore...

Federated learning and deep learning framework for MRI image and speech signal-based multi-modal depression detection.

Computational biology and chemistry
Adolescence is a significant period for developing skills and knowledge and learning about managing relationships and emotions by gathering attributes for maturity. Recently, Depression arises as a common mental health issue in adolescents and this a...

A novel ensemble approach with deep transfer learning for accurate identification of foodborne bacteria from hyperspectral microscopy.

Computational biology and chemistry
The detection of foodborne bacteria is critical in ensuring both consumer safety and food safety. If these pathogens are not properly identified, it can lead to dangerous cross-contamination. One of the most common methods for classifying bacteria is...