Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …

With the remarkable success of representation learning for prediction problems, we have
witnessed a rapid expansion of the use of machine learning and deep learning for the …

Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens
highly depend on the phenotype and topological distribution of constituting histological …

Abstract Gigapixel Whole Slide Images (WSIs) aided patient diagnosis and prognosis
analysis are promising directions in computational pathology. However, limited by …

Digital histopathological images, high‐resolution images of stained tissue samples, are a
vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state …

Abstract Multiple Instance Learning (MIL) methods have become increasingly popular for
classifying gigapixel-sized Whole-Slide Images (WSIs) in digital pathology. Most MIL …

Histopathology is a vital diagnostic discipline in medicine, fundamental to our
understanding, detection, assessment and treatment of conditions such as cancer, dementia …

3D symmetry detection is a fundamental problem in computer vision and graphics. Most
prior works detect symmetry when the object model is fully known, few studies symmetry …

Image analysis and machine learning algorithms operating on multi-gigapixel whole-slide
images (WSIs) often process a large number of tiles (sub-images) and require aggregating …

Advances in entity-graph analysis of histopathology images have brought in a new
paradigm to describe tissue composition, and learn the tissue structure-to-function …