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Thinhinane Megherbi

dblp:141/8943 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2021
0000-0002-1928-7668ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Medical and health informatics › neuroimaging
diffusion MRI
0.112021
Browser-Level Parallelism and Interactive Rendering APIs for Scalable Computation-Intensive SaaS: Application to Brain Diffusion MRI · IEEE Trans. Serv. Comput. 2021

Methods — techniques the papers use, named apart from their topics

parallel diffusion tensor estimation · 1.0browser APIs · 1.0
YearPublicationVenuePosition
2021 Browser-Level Parallelism and Interactive Rendering APIs for Scalable Computation-Intensive SaaS: Application to Brain Diffusion MRI
abstract
Due to their heavy reliance on server infrastructure, the current computation-intensive SaaS suffer from scalability issues compared to the existing data-intensive commercial SaaS. Offloading certain computations to the client browser can resolve these scalability issues but the current Browser APIs are complex to use and integrate in a single software. We propose in this paper four high level APIs that harness existing browser-based paradigms and proven software architectures to reduce the complexity of parallel computing and device-agnostic interactive rendering in the web browser. To allow experimental results, we have developed a proof-of-concept browser-based and interactive diffusion MRI where we have particularly deployed a parallel Diffusion Tensor Estimation. Our platform provides us easy APIs achieving up to 4 times speedup in parallel computation and real-time interactive rendering performances across different Browsers and Devices in comparison to other existing solutions in the Diffusion MRI Community.
Tarik Zakaria Benmerar, Thinhinane Megherbi, Mouloud Kachouane, Rachid Deriche, Fatima Oulebsir-Boumghar
IEEE Trans. Serv. Comput.2
2015 Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use?
Lipeng Ning, Frederik B. Laun, Yaniv Gur, Edward V. R. Di Bella, Samuel Deslauriers-Gauthier, Thinhinane Megherbi, Aurobrata Ghosh, Mauro Zucchelli, Gloria Menegaz, Rutger Fick, Samuel St-Jean, Michael Paquette, Ramón Aranda, Maxime Descoteaux, Rachid Deriche, Lauren O'Donnell, Yogesh Rathi
Medical Image Anal.6