Early and accurate detection and classification of glioma types is of paramount importance
in determining treatment planning and increasing the survival rate of patients. At present …

Background The approaches based on vision transformers (ViTs) are advancing the field of
medical artificial intelligence (AI) and cancer diagnosis. Recently, many researchers have …

In recent years, there is been a growing reliance on image analysis methods to bolster
dentistry practices, such as image classification, segmentation and object detection …

Histopathological images are widely used for the analysis of diseased (tumor) tissues and
patient treatment selection. While the majority of microscopy image processing was …

Deep multiple instance learning (MIL) pipelines are the mainstream weakly supervised
learning methodologies for whole slide image (WSI) classification. However, it remains …

Recent years witnessed remarkable progress in computational histopathology, largely
fueled by deep learning. This brought the clinical adoption of deep learning-based tools …

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

Nuclei classification provides valuable information for histopathology image analysis.
However, the large variations in the appearance of different nuclei types cause difficulties in …

With the advent of digital pathology and microscopic systems that can scan and save whole
slide histological images automatically, there is a growing trend to use computerized …

Recent advances in deep learning have completely transformed the domain of
computational pathology (CPath). More specifically, it has altered the diagnostic workflow of …