Phase-based computational adaptive optics enables artifact-free super-resolution microscopy. [PDF]
Matsuda A +6 more
europepmc +1 more source
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu +11 more
wiley +1 more source
Implementation and Optimization of a Random Illumination Microscope: towards Robustness for Microscopy Core Facility. [PDF]
Soler N +13 more
europepmc +1 more source
Autonomous AI‐Driven Design for Skin Product Formulations
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang +5 more
wiley +1 more source
Progressive Upsampling Generative Adversarial Network with Collaborative Attention for Single-Image Super-Resolution. [PDF]
Lu H, Zhang J, Jing M, Wang Z, Wang W.
europepmc +1 more source
When Biology Meets Medicine: A Perspective on Foundation Models
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu +3 more
wiley +1 more source
Sub-100 nm manipulation of blue light over a large field of view using Si nanolens array. [PDF]
Shi Z, Jiang W, Lu Y, Zhang W.
europepmc +1 more source
Performance of Blind Deconvolution and Super Resolution Image Reconstruction
Seiichi Gohshi, Michikazu Akasu
openaire +1 more source
AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley +1 more source
Deep learning for enhancement of low-resolution and noisy scanning probe microscopy images. [PDF]
Gelman S +9 more
europepmc +1 more source

