Imaging study sheds light on how deep brain stimulation acts on Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder that causes tremors, body stiffness and other difficulties with ...
Frontiers

Traditional machine learning in biomedical image analysis: before you go too deep

Traditional machine learning (TML) algorithms remain indispensable tools for the analysis of biomedical images, offering significant advantages in multimodal data integration, interpretability, ...
IEEE

Adaptive Random Testing of Deep Learning Systems Using Image Hashing

Abstract: In recent years, deep learning (DL) systems have been applied in many areas, including image processing and autonomous driving. Software testing is an important way to ensure the quality of ...
IEEE

Hybrid Deep Learning Models for Remote Sensing Image Processing

Abstract: Core image processing tasks, such as super-resolution, denoising, deblurring, pansharpening, and atmospheric correction, underpin all optical remote sensing (RS) pipelines. Errors at this ...
Beebom

What is Deep Learning in AI? Understanding How AI Really Works

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...
C&EN

Leveraging Deep Learning to Address Diagnostic Challenges with Insufficient Image Data

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. In recent AI-driven disease diagnosis, the success of models has depended mainly on ...
GitHub

Self-Learning-VAE-for-EEG-Artifact-Removal

This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...
Frontiers

Deep learning for steganalysis: evaluating model robustness against image transformations

This study investigates the robustness of deep learning-based steganalysis models against common image transformations because most literature has not paid enough attention to resilience assessment.