Medical Imaging has become a vital technique that has been embraced in the diagnosis and
treatment process of cancer. Histopathological slides, which microscopically examine the …

With the advent of whole slide image (WSI) scanners, pathology is undergoing a digital
revolution. Simultaneously, with the development of image analysis algorithms based on …

Nuclear detection, segmentation and morphometric profiling are essential in hel** us
further understand the relationship between histology and patient outcome. To drive …

While machine learning is currently transforming the field of histopathology, the domain
lacks a comprehensive evaluation of state-of-the-art models based on essential but …

Large amounts of digitized histopathological data display a promising future for develo**
pathological foundation models via self-supervised learning methods. Foundation models …

Deep learning models have exhibited exceptional effectiveness in Computational Pathology
(CPath) by tackling intricate tasks across an array of histology image analysis applications …

Stain variations pose a major challenge to deep learning segmentation algorithms in
histopathology images. Current unsupervised domain adaptation methods show promise in …

With the development of digital pathology, deep learning is increasingly being applied to
endometrial cell morphology analysis for cancer screening. And cytology images with …

Color variations in H&E histological images can impact the segmentation and classification
stages of computational systems used for cancer diagnosis. To address these variations …

Diagnostic histopathology faces increasing demands due to aging populations and
expanding healthcare programs. Semi-automated diagnostic systems employing deep …