Hyperspectral microscopy in biology and minerals, unsupervised deep learning neural
network denoising SRS photos: hyperspectral resolution enhancement and denoising one …

Propelled by the striking advances in optical microscopy and deep learning (DL), the role of
imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a …

Sarcoidosis embodies a complex inflammatory disorder spanning multiple systems, with its
origin remaining elusive. It manifests as the infiltration of inflammatory cells that coalesce …

Existing applications of deep learning in computational imaging and microscopy mostly
depend on supervised learning, requiring large-scale, diverse and labelled training data …

The goal of oncologic surgeries is complete tumor resection, yet positive margins are
frequently found postoperatively using gold standard H&E-stained histology methods …

Optical imaging of metabolism can provide key information about health and disease
progression in cells and tissues; however, current methods have lacked gold-standard …

Pathology is a medical discipline dealing with diagnosing and studying diseases. Through
recognizing structural histological alterations, pathologists acquire valuable information on …

Routine examination for intraoperative histopathologic assessment is lengthy and laborious.
Here, we present the dark-field reflectance ultraviolet microscopy (DRUM) that enables label …

Cancer remains a global health challenge, demanding early detection and accurate
diagnosis for improved patient outcomes. An intelligent paradigm is introduced that elevates …

Deep learning algorithms have been widely used in microscopic image translation. The
corresponding data-driven models can be trained by supervised or unsupervised learning …