Nucleus segmentation is an imperative step in the qualitative study of imaging datasets,
considered as an intricate task in histopathology image analysis. Segmenting a nucleus is …

Digital pathology represents a major evolution in modern medicine. Pathological
examinations constitute the standard in medical protocols and the law, and call for specific …

Emerging spatially resolved transcriptomics technologies allow for the measurement of gene
expression in situ at cellular resolution. We apply direct RNA hybridization-based in situ …

Cancer is the second deadliest disease globally that can affect any human body organ.
Early detection of cancer can increase the chances of survival in humans. Morphometric …

Image analysis in the field of digital pathology has recently gained increased popularity. The
use of high-quality whole-slide scanners enables the fast acquisition of large amounts of …

Separating and labeling each nuclear instance (instance-aware segmentation) is the key
challenge in nuclear image segmentation. Deep Convolutional Neural Networks have been …

Segmenting cell nuclei within microscopy images is a ubiquitous task in biological research
and clinical applications. Unfortunately, segmenting low-contrast overlap** objects that …

Intratumoral drug delivery is a promising approach for the treatment of glioblastoma
multiforme (GBM). However, drug washout remains a major challenge in GBM therapy. Our …

Generative models have been successfully used to synthesize completely novel images,
text, music, and speech. As such, they present an exciting opportunity for the design of new …

The automated assessment of tumors in medical image analysis encounters challenges due
to the resemblance of colon and lung tumors to non-mitotic nuclei and their heteromorphic …