Computer‐aided classification of pathological images is of the great significance for breast
cancer diagnosis. In recent years, deep learning methods for breast cancer pathological …

Breast cancer is one of the most common invading cancers in women. Analyzing breast
cancer is nontrivial and may lead to disagreements among experts. Although deep learning …

This work presents a novel self-supervised pre-training method to learn efficient
representations without labels on histopathology medical images utilizing magnification …

Many individuals worldwide pass away as a result of inadequate procedures for prompt
illness identification and subsequent treatment. A valuable life can be saved or at least …

Histopathological examination is an important criterion in the clinical diagnosis of
osteosarcoma. With the improvement of hardware technology and computing power …

Deep learning approaches are widely applied to histopathological image analysis due to the
impressive levels of performance achieved. However, when dealing with high-resolution …

Cancers are the leading cause of death in many countries. Early diagnosis plays a crucial
role in having proper treatment for this debilitating disease. The automated classification of …

Accurate and timely detection of breast cancer plays a pivotal role in reducing the mortality
rate. Deep learning models, especially CNNs, have recently shown astounding performance …

Breast cancer is the second most common cancer among women worldwide, and the
diagnosis by pathologists is a time-consuming procedure and subjective. Computer-aided …

With the breakthrough performance in a variety of computer vision and medical image
analysis problems, convolutional neural networks (CNNs) have been successfully …