The clinical success of cancer immune checkpoint blockade (ICB) has refocused attention
on tumor-infiltrating lymphocytes (TILs) across cancer types. The outcome of immune …

Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …

Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative
analysis of imaging data for biological and biomedical applications. Many bioimage analysis …

Nuclear segmentation and classification within Haematoxylin & Eosin stained histology
images is a fundamental prerequisite in the digital pathology work-flow. The development of …

With the development of Computer-aided Diagnosis (CAD) and image scanning techniques,
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …

Nuclei mymargin segmentation is a fundamental task for various computational pathology
applications including nuclei morphology analysis, cell type classification, and cancer …

Single-cell segmentation is typically a crucial task of image-based cellular analysis. We
present nucleAIzer, a deep-learning approach aiming toward a truly general method for …

Bio-medical image segmentation is one of the promising sectors where nuclei segmentation
from high-resolution histopathological images enables extraction of very high-quality …

Breast cancer is one of the most common and deadliest cancers among women. Since
histopathological images contain sufficient phenotypic information, they play an …

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 …