Lucian Mihai Itu

TL;DR: In this article, a method for modeling a blood vessel includes: (a) modeling a first segment of the blood vessel based on medical imaging data acquired from a subject; (b) computing a first modeling parameter at an interior point of the first segment; and (c) computing another model at a boundary point of first segment using a viscoelastic wall model.

TL;DR: This paper focuses on recent advancements related to the challenges mentioned above, discusses the industrial impact of proposed solutions, and identifies challenges for future research, and comments on the interaction between the identified challenges in the conclusions.

TL;DR: Recent developments related to explainability and interpretability have become core requirements for AI algorithms, to ensure that the rationale behind output inference can be revealed, and the clinical impact of proposed solutions are discussed.

TL;DR: An image obfuscation algorithm is proposed that combines a variational autoencoder (VAE) with random non-bijective pixel intensity mapping to protect the content of medical images, which are subsequently employed in the development of DL-based solutions.

TL;DR: In this article, a self-taught artificial agent, Vito, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters.