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 ...
science.psu

Deep Learning, Image Processing, and Cyro-EM

From an architect designing a building to a biologist trying to dissect the molecular causes of a disease, it is crucial to understand the relationship between structure and function. At the scale 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 ...
BioSpace

GE HealthCare unveils Pristina Recon DL for mammography – deep learning image reconstruction technology delivering clearer image quality

GE HealthCare has received FDA Premarket Authorization for Pristina Recon DL, an innovative 3D mammography reconstruction application. Powered by artificial intelligence (AI), Pristina Recon DL ...
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 ...
GitHub

Large Language Models (LLMs) with MATLAB®

This repository contains example code to demonstrate how to connect MATLAB to the OpenAI™ Chat Completions API (which powers ChatGPT™) as well as OpenAI Images API (which powers DALL·E™). This allows ...
Frontiers

Editorial: Bio-Inspired Physiological Signal(s) and Medical Image(s) Neural Processing Systems Based on Deep Learning and Mathematical Modeling for Implementing Bio-Engineering ...

This Research Topic gathers different contributions highlighting novel types of bio-inspired mathematical models with neuroimaging and physio-signal data, mainly applied in medical and industrial ...