Researches on breast cancer histopathological image classification have achieved a great
breakthrough using deep backbones of Convolutional Neural Networks (CNNs) in recent …

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

Breast cancer histopathological image classification has made great progress with the use
of Convolutional Neural Networks (CNNs). However, due to the limited receptive field, CNNs …

One issue with computer based histopathology image analysis is that the size of the raw
image is usually very large. Taking the raw image as input to the deep learning model would …

Breast cancer is one of the most prevalent cancers in women. Reliable pathology
identification can help histopathologists make accurate diagnosis of breast cancer but …

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 …

Histopathological image classification has become one of the most challenging tasks among
researchers due to the fine-grained variability of the disease. However, the rapid …

Breast cancer is associated with the highest morbidity rates for cancer diagnoses in the
world and has become a major public health issue. Early diagnosis can increase the chance …

Background Breast cancer causes hundreds of thousands of deaths each year worldwide.
The early stage diagnosis and treatment can significantly reduce the mortality rate. However …

Breast cancer is a global health concern, with approximately 30 million new cases projected
to be reported by 2030. While efforts are being channeled into curative measures …