Machine learning classification of normal and malignant cells on the basis of their viscoelastic properties

Tracheostomy weaning in patients with severe acquired brain injury: External validation of machine learning models

Piergiuseppe Liuzzi, Bahia Hakiki, Francesca Draghi, Agnese De Nisco, Anna Maria Romoli, Daniela Maccanti, et al.

Clustering beyond the binary: Stable machine-learning phenotypes of metabolic syndrome

Jennifer Wilkie, Xu-Feng Huang, Lei Wang, Khin Than Win

Machine learning-based extraction of microstructural parameters from diffusion-weighted imaging starting from realistic in silico cellular substrates: Application in breast and prostate cancer

Chiara Tinelli, Chiara Scotti, Fabio Casaccio, Marko Zlatic, Guido Baroni, Letizia Morelli, et al.

The latent shape space of intracranial saccular aneurysms.

P. Eulzer, Henrik Voigt, Monique Meuschke, Kai Lawonn

TL;DR: A unified framework that learns a compact but expressive latent representation of aneurysm morphology for generative modeling and rupture-label classification is developed, providing a scalable and interpretable basis for quantitative aneurysm morphometry.

BACKGROUND AND OBJECTIVE Underlying biomechanical instability of the vessel wall is believed to drive the substantial morphological variability observed in saccular intracranial aneurysms. Existing approaches to quantify this shape variance rely largely on handcrafted descriptors…Read more

Deep learning-based segmentation of human oocytes with cross-dataset evaluation

Zhilin Lei, Xiaohu Xu, Liza Tilia, Sarah du Toit-Thompson, Fabrizzio Horta, Robert B. Gilchrist, et al.