Proceedings of the National Academy of Sciences (PNAS)

Identifying variation in dinosaur footprints and classifying problematic specimens via unbiased unsupervised machine learning

Machine learning holds great promise for classifying and identifying fossils, and has recently been marshaled to identify trackmakers of dinosaur ...
Phys.org

New X-ray method captures 3 image-contrast types in a single shot

University of Houston researchers developed a new X-ray imaging method capable of revealing hidden features in a single shot, a breakthrough that could advance cancer detection, disease monitoring, ...
Frontiers

UDRNet: unsupervised deformable registration network of lung CT images with hybrid attention mechanism

The First Affiliated Hospital of Dalian Medical University, Dalian, China With the continuous updates and iterations of diagnostic equipment and technologies, the diagnosis of lung diseases has ...
Frontiers

Training marine species object detectors with synthetic images and unsupervised domain adaptation

1 Australian Centre for Robotics, University of Sydney, Sydney, NSW, Australia 2 Department of Marine Technology, Norwegian University of Science and Technology, Trondheim, Norway Visual surveys by ...
Scientific Research Publishing

Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications ()

The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in unsupervised ...
IEEE

Neural Architecture Search for Unsupervised PET Image Denoising

Abstract: Unsupervised learning methods effectively reduce the noise level of positron emission tomography (PET) images with limited training data. Recent research indicates that the performance of ...
marktechpost

Foundation Models No Longer Need Prompts or Labels: EPFL Researchers Introduce a Joint Inference Framework for Fully Unsupervised Adaptation Using Fine-Tuning and In-Context ...

Foundation models, often massive neural networks trained on extensive text and image data, have significantly shifted how artificial intelligence systems handle language and vision tasks. These models ...
IEEE

A Sensitivity-Guided Unsupervised Learning Method for Image Reconstruction of Electrical Impedance Tomography

Abstract: Electrical impedance tomography (EIT) detects time-varying conductivity distribution and has grown to be a promising imaging modality in industrial and biomedical fields. However, current ...