Colorectal cancer (CRC) is the second most common cancer in women and the third most
common in men, with an increasing incidence. Pathology diagnosis complemented with …

Deep learning (DL) has become one of the major approaches in computational
dermatopathology, evidenced by a significant increase in this topic in the current literature …

A large-scale and well-annotated dataset is a key factor for the success of deep learning in
medical image analysis. However, assembling such large annotations is very challenging …

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …

The accelerated adoption of digital pathology and advances in deep learning have enabled
the development of robust models for various pathology tasks across a diverse array of …

The adoption of digital pathology has enabled the curation of large repositories of gigapixel
whole-slide images (WSIs). Computationally identifying WSIs with similar morphologic …

Intelligent agents are showing increasing promise for clinical decision-making in a variety of
healthcare settings. While a substantial body of work has contributed to the best strategies to …

In this paper, we developed BreastScreening-AI within two scenarios for the classification of
multimodal beast images:(1) Clinician-Only; and (2) Clinician-AI. The novelty relies on the …

Digital pathology allows computerized analysis of tumor ecosystem using whole slide
images (WSIs). Here, we present single-cell morphological and topological profiling (sc …

Current representation learning methods for whole slide image (WSI) with pyramidal
resolutions are inherently homogeneous and flat, which cannot fully exploit the multiscale …