Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

Machine learning techniques used in computer-aided medical image analysis usually suffer
from the domain shift problem caused by different distributions between source/reference …

Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …

The semantic image segmentation task consists of classifying each pixel of an image into an
instance, where each instance corresponds to a class. This task is a part of the concept of …

The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …

Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …

Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

Recent advances in deep learning for medical image segmentation demonstrate expert-
level accuracy. However, application of these models in clinically realistic environments can …

Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …

In this paper, we first describe the basics of the field of cancer diagnosis, which includes
steps of cancer diagnosis followed by the typical classification methods used by doctors …