Unsupervised multilabel image segmentation (colour, grayscale, multichannel) via the Potts model — also known as the piecewise-constant Mumford-Shah model or the ℓ⁰ gradient model. Solvers for 1-D ...

This is the authors' PyTorch implementation of CUTS, MICCAI 2024. The official version is maintained in the Lab GitHub repo. Please be mindful that these datasets are ...

Corrosion poses a substantial economic burden, and machine learning is increasingly being explored for its potential in staging, predictive maintenance, and data-driven decision making. This study ...

Abstract: Medical image segmentation is an important task in medical imaging, as it serves as the first step for clinical diagnosis and treatment planning. While major success has been reported using ...

Fawad is a Telecommunications Engineer and a cybersecurity writer at MUO. He has been writing on security and privacy topics since 2017 for publications like WizCase, vpnMentor, and many others. He ...

Accurate brain segmentation is critical for magnetic resonance imaging (MRI) analysis pipelines. Machine-learning-based brain MR image segmentation methods are among the state-of-the-art techniques ...

Laser speckle contrast imaging (LSCI) is a full-field, high spatiotemporal resolution and low-cost optical technique for measuring blood flow, which has been successfully used for neurovascular ...

The world is getting “smarter” every day, and to keep up with consumer expectations, companies are increasingly using machine learning algorithms to make things easier. You can see them in use in ...

1 Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China. 2 Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; ...

Abstract: The usage of convolutional neural networks (CNNs) for unsupervised image segmentation was investigated in this study. Similar to supervised image segmentation, the proposed CNN assigns ...