Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …

Modern day computer vision tasks requires efficient solution to problems such as image
recognition, natural language processing, object detection, object segmentation and …

Medical image classification plays an essential role in clinical treatment and teaching tasks.
However, the traditional method has reached its ceiling on performance. Moreover, by using …

Breast cancer is the most common invasive cancer in women, affecting more than 10% of
women worldwide. Microscopic analysis of a biopsy remains one of the most important …

Carcinoma is a primary source of morbidity in women globally, with metastatic disease
accounting for most deaths. Its early discovery and diagnosis may significantly increase the …

Diabetic retinopathy is one of the major causes of blindness in the population aged 20–65.
In this paper, we address the problem of automatic diabetic retinopathy detection and …

The revolution in computer hardware, especially in graphics processing units and tensor
processing units, has enabled significant advances in computer graphics and artificial …

Automatic classification of breast cancer histopathological images is of great application
value in breast cancer diagnosis. Convolutional neural network (CNN) usually highlights …

The medical field is creating large amount of data that physicians are unable to decipher
and use efficiently. Moreover, rule-based expert systems are inefficient in solving …

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 …