Background: Trajectory modeling is a longstanding challenge in the application of
computational methods to healthcare. However, traditional statistical and machine learning …

Recent self-supervised advances in medical computer vision exploit the global and local
anatomical self-similarity for pretraining prior to downstream tasks such as segmentation …

This paper targets on self-supervised tumor segmentation. We make the following
contributions:(i) we take inspiration from the observation that tumors are often characterised …

Registration is the process of spatially and/or temporally aligning different images. It is a
critical tool that can facilitate the automatic tracking of pathological changes detected in …

The detection of structural chromosomal abnormalities (SCA) is crucial for diagnosis,
prognosis and management of many genetic diseases and cancers. This detection, done by …

In this paper, we target self-supervised representation learning for zero-shot tumor
segmentation. We make the following contributions: First, we advocate a zero-shot setting …

Deep learning models have found extensive application in medical imaging analysis,
particularly in mammography classification. However, these models encounter challenges …

Multiple sclerosis (MS) is a chronic neurological disorder characterized by the development
of lesions in the central nervous system, typically occurring in the brain whose diagnosis and …

Deep learning has developed as a great tool for many computer vision or natural language
processing tasks. However, supervised deep learning algorithms require a large amount of …

Multiple Sclerosis (MS) can be early diagnosed by detecting lesions in brain magnetic
resonance images (MRI). Unsupervised anomaly detection methods based on autoencoder …