Most oncological cases can be detected by imaging techniques, but diagnosis is based on
pathological assessment of tissue samples. In recent years, the pathology field has evolved …

Cameras in modern devices such as smartphones, satellites and medical equipment are
capable of capturing very high resolution images and videos. Such high-resolution data …

Digitizing whole-slide imaging in digital pathology has led to the advancement of computer-
aided tissue examination using machine learning techniques, especially convolutional …

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

Digital pathology coupled with advanced machine learning (eg, deep learning) has been
changing the paradigm of whole-slide histopathological images (WSIs) analysis. Major …

Lung cancer is the leading cause of death for men and women worldwide and the second
most frequent cancer. Therefore, early detection of the disease increases the cure rate. This …

Purpose Semantic segmentation in high-resolution, histopathology whole slide images
(WSIs) is an important fundamental task in various pathology applications. Convolutional …

Breast cancer diagnosis in multi-gigapixel whole slide images (WSIs) is an important task
that highly relevant to cancer grading and prognosis. In recent years, many computer-aided …

As the technological advances in computer hardware and machine learning have increased
significantly, deep learning models have also been used in many different areas. Examples …

Since signet ring cells (SRCs) are associated with high peripheral metastasis rate and
dismal survival, they play an important role in determining surgical approaches and …