A Kazerouni, EK Aghdam, M Heidari, R Azad… - arXiv preprint arXiv …, 2022 - arxiv.org
Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward …
Imputation of missing images via source-to-target modality translation can improve diversity in medical imaging protocols. A pervasive approach for synthesizing target images involves …
O Dalmaz, M Yurt, T Çukur - IEEE Transactions on Medical …, 2022 - ieeexplore.ieee.org
Generative adversarial models with convolutional neural network (CNN) backbones have recently been established as state-of-the-art in numerous medical image synthesis tasks …
T Wang, Y Lei, Y Fu, JF Wynne… - Journal of applied …, 2021 - Wiley Online Library
This paper reviewed the deep learning‐based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning …
Despite advances in data augmentation and transfer learning, convolutional neural networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans …
Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in hospitals across the world. These have the potential to revolutionize our understanding of …
Data diversity is critical to success when training deep learning models. Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces …
S Soffer, A Ben-Cohen, O Shimon, MM Amitai… - Radiology, 2019 - pubs.rsna.org
Deep learning has rapidly advanced in various fields within the past few years and has recently gained particular attention in the radiology community. This article provides an …
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 …