Ali Kamen

TL;DR: This paper investigates in this paper how DL could help organ-specific (ROI-specific) deformable registration, to solve motion compensation or atlas-based segmentation problems for instance in prostate diagnosis and presents a training scheme with a large number of synthetically deformed image pairs requiring only a small number of real inter-subject pairs.

TL;DR: In this paper, a method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed, where a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value is determined based on the extracted set of feature using a trained machine-learning based mapping.

TL;DR: This paper proposes a fully unsupervised multi-modal deformable image registration method (UMDIR), which does not require any ground truth deformation fields or any aligned multi- modal image pairs during training, and achieves competitive performance against other methods at substantially reduced computation time.

TL;DR: The results indicate that there are image-distinct subpopulations that have differential sensitivity to radiotherapy and show that i Gray, an individualised dose that estimates treatment failure probability to be below 5%, can be safely delivered in the majority of cases.

TL;DR: In this article, an artificial agent is learned, modeled using deep convolutional neural networks, with 3D raw image data as the input, and the next optimal action as the output.