Generative adversarial networks - Open Access .click

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Generative Adversarial Networks for Creating Synthetic Nucleic Acid Sequences of Cat Genome [PDF]

, 2022
Debapriya Hazra, Mi-Ryung Kim, Yung-Cheol Byun +2 more
openalex +1 more source

Upsampling DINOv2 Features for Unsupervised Vision Tasks and Weakly Supervised Materials Segmentation

Advanced Intelligent Systems, EarlyView.
Feature from recent image foundation models (DINOv2) are useful for vision tasks (segmentation, object localization) with little or no human input. Once upsampled, they can be used for weakly supervised micrograph segmentation, achieving strong results when compared to classical features (blurs, edge detection) across a range of material systems.
Ronan Docherty, Antonis Vamvakeros, Samuel J. Cooper +2 more
wiley +1 more source

Restoration of marker occluded hematoxylin and eosin stained whole slide\n histology images using generative adversarial networks [PDF]

, 2019
Bairavi Venkatesh +3 more
openalex +1 more source

“Will you be there for me?” Social support from family and friends during cold case sexual assault prosecutions

American Journal of Community Psychology, EarlyView.
Abstract If sexual assault survivors report the assault to the criminal legal system, they often need informal support from family and friends throughout the long and frequently retraumatizing process of investigation and prosecution. This study is part of a long‐term community‐based participatory action research project in a predominately Black ...
Rebecca Campbell +4 more
wiley +1 more source

Artificial Scanning Electron Microscopy Images Created by Generative Adversarial Networks from Simulated Particle Assemblies [PDF]

, 2023
Jonas Bals, Matthias Epple
openalex +1 more source

Prioritizing Feasible and Impactful Actions to Enable Secure AI Development and Use in Biology

Biotechnology and Bioengineering, EarlyView.
ABSTRACT As artificial intelligence continues to enhance biological innovation, the potential for misuse must be addressed to fully unlock the potential societal benefits. While significant work has been done to evaluate general‐purpose AI and specialized biological design tools (BDTs) for biothreat creation risks, actionable steps to mitigate the risk
Josh Dettman +4 more
wiley +1 more source

Cutting edge strategies for diabetic wound care: Nanotechnology, bioengineering, and beyond

BMEMat, EarlyView.
Graphical abstract illustrates the challenges in diabetic wound healing, covering pathophysiology, formulation hurdles, and emerging therapeutic strategies. It highlights the role of hyperglycemia, formulation complexities, and advanced technologies like bioprinting and AI in improving diabetic wound management. Abstract Diabetic wounds affect millions
Usama Ahmad +8 more
wiley +1 more source

Advancing design strategies in smart stimulus‐responsive liposomes for drug release and nanomedicine

BMEMat, EarlyView.
Schematic illustration of stimulus‐responsive liposomes designed for controlled drug release and nanomedicine. The innermost circle represents different liposomal structures, including unilamellar, multilamellar, and multivesicular liposomes. The middle layer illustrates the responsive phospholipid components.
Yuchen Guo +9 more
wiley +1 more source

Neural architecture search using network embedding and generative adversarial networks. [PDF]

Sci Rep
Yousefi M, Mehrdad V, Dowlatshahi MB.
europepmc +1 more source

Optimized segmentation of the choriocapillaris in ophthalmic optical coherence tomography angiography (OCTA) scans via generative adversarial networks

, 2020
Warren Lewis +3 more
openalex +1 more source

artificial intelligence
computer science
generative grammar

adversarial system
pattern recognition psychology
deep learning

machine learning
generative adversarial network
image mathematics