eLife

Celldetective, an AI-enhanced image analysis tool for unraveling dynamic cell interactions

Celldetective is an open-source software integrating segmentation, tracking, and event detection to perform high-throughput end-to-end study of dynamic cell interactions, without requiring coding ...
Technology Networks

Deep Learning for Variant Calling: How DeepVariant and Neural Networks Are Improving Genomic Accuracy

Deep learning variant calling has transformed genomic accuracy. Discover how DeepVariant works, outperforms classical tools, ...
technologynetworks

Multiomics Data Integration With Machine Learning: Methods, Tools, and Biological Applications

Multiomics data integration with machine learning has become the standard approach for combining genomic, transcriptomic, proteomic, and metabolomic measurements collected from the same biological ...
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 ...
Intelligent CIO

Classiq and UC Chile launch quantum AI research for biomedical image analysis

Classiq and Pontificia Universidad Católica de Chile (UC Chile) have announced a joint research project to develop hybrid quantum algorithms for biomedical image analysis – assisted by classical ...

MicroCloud Hologram Inc. Develops Approximate Quantum State Preparation and Entanglement-Dependent Complexity Algorithm Technology

MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the “Company”), a technology service provider, has announced a groundbreaking achievement of great theoretical and engineering significance: its ...

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 ...
GitHub

[CCS'25] IGMU: Rethinking Machine Unlearning in Image Generation Models

To support a wider range of unlearning tasks, we have introduced a new checker multi_multiC. Unlike the previous bi_multiC, which was limited to binary classification, the new multi_multiC supports ...
bjo.bmj

Realistic fundus photograph generation for improving automated disease classification

Aims This study aims to investigate whether denoising diffusion probabilistic models (DDPMs) could generate realistic retinal images, and if they could be used to improve the performance of a deep ...
IEEE

Image Recognition & Classification with Machine Learning

Abstract: Image recognition and classification, powered by machine learning, is a rapidly advancing field with broad applications across various industries. Machine learning techniques, especially ...
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, ...