Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …

Deep learning has become the most widely used approach for cardiac image segmentation
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …

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 …

RGB-Infrared (IR) person re-identification is an important and challenging task due to large
cross-modality variations between RGB and IR images. Most conventional approaches aim …

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 …

Although having achieved great success in medical image segmentation, deep learning-
based approaches usually require large amounts of well-annotated data, which can be …

The generalization capability of neural networks across domains is crucial for real-world
applications. We argue that a generalized object recognition system should well understand …

Deep learning models usually suffer from the domain shift issue, where models trained on
one source domain do not generalize well to other unseen domains. In this work, we …

Whole abdominal organ segmentation is important in diagnosing abdomen lesions,
radiotherapy, and follow-up. However, oncologists' delineating all abdominal organs from …

Automated prostate segmentation in MRI is highly demanded for computer-assisted
diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress …