Colorectal adenocarcinoma originating in intestinal glandular structures is the most common
form of colon cancer. In clinical practice, the morphology of intestinal glands, including …

Deep learning (DL) has achieved state-of-the-art performance in many digital pathology
analysis tasks. Traditional methods usually require hand-crafted domain-specific features …

The morphology of glands has been used routinely by pathologists to assess the
malignancy degree of adenocarcinomas. Accurate segmentation of glands from histology …

Abstract Convolutional Neural Networks (CNN) are state-of-the-art models for many image
classification tasks. However, to recognize cancer subtypes automatically, training a CNN on …

In histopathological image analysis, the morphology of histological structures, such as
glands and nuclei, has been routinely adopted by pathologists to assess the malignancy …

Labeling a histopathology image as having cancerous regions or not is a critical task in
cancer diagnosis; it is also clinically important to segment the cancer tissues and cluster …

Tumor segmentation in whole-slide images of histology slides is an important step towards
computer-assisted diagnosis. In this work, we propose a tumor segmentation framework …

Colorectal cancer has a high mortality rate that continuously affects human life globally.
Early detection of it extends human life and helps in preventing disease. Histopathological …

Computerized analysis of digital pathology images offers the potential of improving clinical
care (eg automated diagnosis) and catalyzing research (eg discovering disease subtypes) …

With a mortality rate of approximately 33.33%, Colorectal cancer serves as the second most
prevalent malignant tumor type in the world. AI-guided clinical care/tool can help in reducing …