Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …

Artificial intelligence (AI) for prostate magnetic resonance imaging (MRI) is starting to play a
clinical role for prostate cancer (PCa) patients. AI-assisted reading is feasible, allowing …

Diffusion probabilistic models (DPMs) which employ explicit likelihood characterization and
a gradual sampling process to synthesize data, have gained increasing research interest …

Multi-modal medical image segmentation is a crucial task in oncology that enables the
precise localization and quantification of tumors. The aim of this work is to present a meta …

In healthcare, a growing number of physicians and support staff are striving to facilitate
personalised radiotherapy regimens for patients with prostate cancer. This is because …

In this paper, we present a novel method for the automatic classification of medical images
that learns and leverages weak causal signals in the image. Our framework consists of a …

Objective To evaluate the effectiveness of a self-adapting deep network, trained on large-
scale bi-parametric MRI data, in detecting clinically significant prostate cancer (csPCa) in …

Background Image quality evaluation of prostate MRI is important for successful
implementation of MRI into localized prostate cancer diagnosis. Purpose To examine the …

Magnetic Resonance (MR) images suffer from spatial inhomogeneity, known as bias field
corruption. The N4ITK filter is a state-of-the-art method used for correcting the bias field to …

Background Aging is the most important risk factor for prostate cancer (PC). Imaging
techniques can be useful to measure age‐related changes associated with the transition to …