Brain cancer is one of those few cancers with very high mortality and low five-year survival
rate. First and foremost reason for the woes is the difficulty in diagnosing and monitoring the …

Learning high-quality, self-supervised, visual representations is essential to advance the
role of computer vision in biomedical microscopy and clinical medicine. Previous work has …

Gliomas are infiltrative brain tumors that often involve functional tissue. While maximal safe
resection is critical for maximizing survival, this is challenged by the difficult intraoperative …

A critical challenge in glioma treatment is detecting tumour infiltration during surgery to
achieve safe maximal resection,–. Unfortunately, safely resectable residual tumour is found …

Volumetric biomedical microscopy has the potential to increase the diagnostic information
extracted from clinical tissue specimens and improve the diagnostic accuracy of both human …

Raman imaging (RI) is an outstanding technique that enables molecular-level medical
diagnostics and therapy assessment by providing characteristic fingerprint and …

Whole slide imaging is fundamental to biomedical microscopy and computational pathology.
Previously, learning representations for gigapixel-sized whole slide images (WSIs) has …

Self-supervised representation learning has been highly promising for histopathology image
analysis with numerous approaches leveraging their patient-slide-patch hierarchy to learn …

High-quality, high-resolution medical imaging is essential for clinical care. Raman-based
biomedical optical imaging uses non-ionizing infrared radiation to evaluate human tissues in …

Recent advances in deep learning have completely transformed the domain of
computational pathology (CPath). More specifically, it has altered the diagnostic workflow of …