Automatic medical imaging segmentation via self-supervising large-scale convolutional neural networks.

Purpose: This study aims to develop a robust, large-scale deep learning model for medical image segmentation, leveraging self-supervised learning to overcome the limitations of supervised learning and data variability in clinical settings.

Methods And Materials: We curated a substantial multi-center CT dataset for self-supervised pre-training using masked image modeling with sparse submanifold convolution. We designed a series of Sparse Submanifold U-Nets (SS-UNets) of varying sizes and performed self-supervised pre-training.

Expressive dynamics models with nonlinear injective readouts enable reliable recovery of latent features from neural activity.

The advent of large-scale neural recordings has enabled new approaches that aim to discover the computational mechanisms of neural circuits by understanding the rules that govern how their state evolves over time. While these cannot be directly measured, they can typically be approximated by low-dimensional models in a latent space. How these models represent the mapping from latent space to neural space can affect the interpretability of the latent representation.

Advances in Clinical and Basic Science of Coagulation: Illustrated abstracts of the 9th Chapel Hill Symposium on Hemostasis.

This 9th Symposium on Hemostasis is an international scientific meeting held biannually in Chapel Hill, North Carolina. The meeting is in large measure the result of the close friendship between the late Dr. Harold R.

Gold nanorods: from synthesis and properties to biological and biomedical applications.

Noble metal nanoparticles are capable of confining resonant photons in such a manner as to induce coherent surface plasmon oscillation of their conduction band electrons, a phenomenon leading to two important properties. Firstly, the confinement of the photon to the nanoparticle's dimensions leads to a large increase in its electromagnetic field and consequently great enhancement of all the nanoparticle's radiative properties, such as absorption and scattering. Moreover, by confining the photon's wavelength to the nanoparticle's small dimensions, there exists enhanced imaging resolving powers, which extend well below the diffraction limit, a property of considerable importance in potential device applications.