Histopathological images provide a gold standard for cancer recognition and diagnosis.
Existing approaches for histopathological image classification are supervised learning …

Semantic segmentation of various tissue and nuclei types in histology images is
fundamental to many downstream tasks in the area of computational pathology (CPath). In …

Drug sensitivity prediction models can aid in personalising cancer therapy, biomarker
discovery, and drug design. Such models require survival data from randomised controlled …

A tumor is an abnormal growth of cells, either cancerous or benign, that develops in an
organ. Early detection and segmentation of brain tumors are crucial for effective treatment …

Nuclei detection is a fundamental analytical step in digital histopathology image analysis.
Since labeling the centroids for each nucleus in histopathology images is extremely time …

Nuclei detection is a fundamental task for numerous downstream analysis of histopathology
images. Usually, it requires a large number of labeled images for fully supervised nuclei …

Stain normalization algorithms aim to transform the color and intensity characteristics of a
source multi-gigapixel histology image to match those of a target image, mitigating …

Timely and precise classification and segmentation of gastric bleeding in endoscopic
imagery are pivotal for the rapid diagnosis and intervention of gastric complications, which is …

With the rapid development of smart healthcare, digital histopathological images are playing
an increasingly important role in disease diagnosis. Due to factors such as diverse …

Despite efforts in human papillomavirus (HPV) prevention and screening, cervical cancer
remains the fourth most prevalent cancer among women globally. In this study, we propose …