Deep learning reveals nanoparticle shape from routine tracking analysis without new hardware

Researchers at the University of Tokyo and the Innovation Center of NanoMedicine (iCONM) have developed an artificial ...
Frontiers

Artificial Intelligence and Multimodal Imaging of the Temporomandibular Joint: From Morphology to Clinical Decision Support

The field of orofacial medicine increasingly recognizes the temporomandibular joint (TMJ) as a complex anatomical and functional unit whose disorders can ...
Tech Xplore

Reliably detecting and clearly explaining deepfake images

Artificial intelligence can now generate images that are virtually indistinguishable from real ones. Researchers at the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation ...
News-Medical.Net

Advancing 3D imaging, automation, and phenotypic discovery with AI-driven high-content screening

In this interview, AZoLife Sciences speaks with Boyd Butler, a microscopy and high-content screening expert at Molecular ...
Frontiers

Deep Domain Adaptation Applied to Remote Sensing Data Analysis

Deep learning has transformed remote sensing, driving state-of-the-art results in land use and land cover classification, ...
The Manila Times

WiMi Develops Quantum Convolutional Neural Network Model for Classical Data Classification

WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ('WIMI' or the 'Company'), a leading global Hologram Augmented Reality ('AR') Technology provider, has completed systematic benchmark testing on fully ...
IEEE

A Lightweight Spatial–Spectral Deformable CNN for UAV Hyperspectral Image Classification

Abstract: In recent years, uncrewed aerial vehicle (UAV) technology has shown great potential for application in hyperspectral image (HSI) classification tasks due to its advantages of flexible ...
IEEE

Glycemic Index Classification in Food Images: A Deep Transfer Learning Approach

Abstract: This paper presents an innovative approach to categorize food images based on their glycemic index (GI) value using deep transfer learning. By employing pre-trained models, we aim to enable ...