Zheyuan Zhang

Northwestern University | NU · Department of Biomedical Engineering

Though impressive success has been witnessed in computer vision, deep learning still suffers from the domain shift challenge when the target domain for testing and the source domain for training do not share an identical distribution. To address this, domain generalization approaches intend to extract domain invariant features that can lead to a mo...

Transformer-based neural networks have surpassed promising performance on many biomedical image segmentation tasks due to a better global information modeling from the self-attention mechanism. However, most methods are still designed for 2D medical images while ignoring the essential 3D volume information. The main challenge for 3D Transformer-bas...

Automated liver segmentation from radiology scans (CT, MRI) can improve surgery and therapy planning and follow-up assessment in addition to conventional use for diagnosis and prognosis. Although convolution neural networks (CNNs) have became the standard image segmentation tasks, more recently this has started to change towards Transformers based...

Transformer-based neural networks have surpassed promising performance on many biomedical image segmentation tasks due to a better global information modeling from the self-attention mechanism. However, most methods are still designed for 2D medical images while ignoring the essential 3D volume information. The main challenge for 3D transformer-bas...

Automated liver segmentation from radiology scans (CT, MRI) can improve surgery and therapy planning and follow-up assessment in addition to conventional use for diagnosis and prognosis. Although convolutional neural networks (CNNs) have become the standard image segmentation tasks, more recently this has started to change towards Transformers base...

Super-resolution microscopy revolutionized biomedical research with significantly improved imaging resolution down to the molecular scale. To date, only limited studies reported multi-color super-resolution imaging of thin tissue slices mainly because of unavailable staining protocols and incompatible imaging techniques. Here, we show the first sup...

Spectroscopic single-molecule localization microscopy (sSMLM) simultaneously captures the spatial locations and emission spectra of single molecular emissions and enables simultaneous multicolor super-resolution imaging. Existing sSMLM relies on extracting spectral signatures, such as weighted spectral centroids, to distinguish different molecular...

We propose a method to improve the holographic performance of a volume holographic material by the particle-shape dependence of doped nanoparticles. Previously reported methods are based on changing the doping concentration of dopants and the diameter of nanoparticles or modifying the surface of nanoparticles. When transverse surface plasmon resona...