AIMC Topic: Deep Learning

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Stain-Independent Deep Learning-Based Analysis of Digital Kidney Histopathology.

The American journal of pathology
Convolutional neural network (CNN)-based image analysis applications in digital pathology (eg, tissue segmentation) require a large amount of annotated data and are mostly trained and applicable on a single stain. Here, a novel concept based on stain...

Artificial Intelligence and Deep Learning for Rheumatologists.

Arthritis & rheumatology (Hoboken, N.J.)
Deep learning has emerged as the leading method in machine learning, spawning a rapidly growing field of academic research and commercial applications across medicine. Deep learning could have particular relevance to rheumatology if correctly utilize...

Deep-learning-based hepatic fat assessment (DeHFt) on non-contrast chest CT and its association with disease severity in COVID-19 infections: A multi-site retrospective study.

EBioMedicine
BACKGROUND: Hepatic steatosis (HS) identified on CT may provide an integrated cardiometabolic and COVID-19 risk assessment. This study presents a deep-learning-based hepatic fat assessment (DeHFt) pipeline for (a) more standardised measurements and (...

Deep Learning Hybrid Techniques for Brain Tumor Segmentation.

Sensors (Basel, Switzerland)
Medical images play an important role in medical diagnosis and treatment. Oncologists analyze images to determine the different characteristics of deadly diseases, plan the therapy, and observe the evolution of the disease. The objective of this pape...

Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning.

Sensors (Basel, Switzerland)
Classification of fruit and vegetable freshness plays an essential role in the food industry. Freshness is a fundamental measure of fruit and vegetable quality that directly affects the physical health and purchasing motivation of consumers. In addit...

Towards Transfer Learning Techniques-BERT, DistilBERT, BERTimbau, and DistilBERTimbau for Automatic Text Classification from Different Languages: A Case Study.

Sensors (Basel, Switzerland)
The Internet of Things is a paradigm that interconnects several smart devices through the internet to provide ubiquitous services to users. This paradigm and Web 2.0 platforms generate countless amounts of textual data. Thus, a significant challenge ...

Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method.

Sensors (Basel, Switzerland)
Determining environmental chemical carcinogenicity is urgently needed as humans are increasingly exposed to these chemicals. In this study, we developed a hybrid neural network (HNN) method called HNN-Cancer to predict potential carcinogens of real-l...

Deep learning-based diagnosis of Alzheimer's disease using brain magnetic resonance images: an empirical study.

Scientific reports
The limited accessibility of medical specialists for Alzheimer's disease (AD) can make obtaining an accurate diagnosis in a timely manner challenging and may influence prognosis. We investigated whether VUNO Med-DeepBrain AD (DBAD) using a deep learn...