Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …

Deep learning techniques have promoted the rise of artificial intelligence (AI) and performed
well in computer vision. Medical image analysis is an important application of deep learning …

Multi-modal Magnetic Resonance Imaging (MRI) can provide complementary information for
automatic brain tumor segmentation, which is crucial for diagnosis and prognosis. While …

Reducing the requirement for densely annotated masks in medical image segmentation is
important due to cost constraints. In this paper, we consider the problem of inferring pixel …

Adversarial attacks are considered a potentially serious security threat for machine learning
systems. Medical image analysis (MedIA) systems have recently been argued to be …

Chest computed tomography (CT) image data is necessary for early diagnosis, treatment,
and prognosis of Coronavirus Disease 2019 (COVID-19). Artificial intelligence has been …

Supervised learning of every possible pathology is unrealistic for many primary care
applications like health screening. Image anomaly detection methods that learn normal …

To extract meaningful and reproducible models of brain function from stroke images, for both
clinical and research proposes, is a daunting task severely hindered by the great variability …

The volume of medical images stored in hospitals is rapidly increasing; however, the
utilization of these accumulated medical images remains limited. Existing content-based …

In medical imaging, the characteristics purely derived from a disease should reflect the
extent to which abnormal findings deviate from the normal features. Indeed, physicians often …