The aim of this study is to review the methods being used for image analysis of microscopic
views of immunohistochemically stained specimen in medical research. The solutions …

Colorectal cancer (CRC) grading is typically carried out by assessing the degree of gland
formation within histology images. To do this, it is important to consider the overall tissue …

Computer aided fusion of multi-modality medical images provides a very promising
diagnostic tool with numerous clinical applications. The objective of this paper is to present …

In traditional cancer diagnosis, pathologists examine biopsies to make diagnostic
assessments largely based on cell morphology and tissue distribution. However, this is …

Background/Aims To develop a deep learning system (DLS) that can automatically detect
malignant melanoma (MM) in the eyelid from histopathological sections with colossal …

We consider the problem of automated cancer diagnosis in the context of breast tissues. We
present graph theoretical techniques that identify and compute quantitative metrics for tissue …

Histopathological examination is a powerful standard for the prognosis of critical diseases.
But, despite significant advances in high-speed and high-resolution scanning devices or in …

This work presents a graph-based representation (aka, cell-graph) of histopathological
images for automated cancer diagnosis by probabilistically assigning a link between a pair …

This work reports a novel computational method based on augmented cell-graphs (ACG),
which are constructed from low-magnification tissue images for the mathematical diagnosis …

Pathological examination of a biopsy is the most reliable and widely used technique to
diagnose bone cancer. However, it suffers from both inter-and intra-observer subjectivity …