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Joaquim Salvi

Joaquim Salvi

D-Index & Metrics

Computer Science

D-Index
39
Citations
12547
World Ranking
9510
National Ranking
148

Overview

Joaquim Salvi is affiliated with the University of Girona in Spain and has contributed extensively to research at the intersection of medicine and computer science. Their work predominantly centers on neurology and medical imaging, with a significant focus on brain-related conditions and computational methods to support diagnosis and treatment.

Their research spans a range of topics that include brain tumor detection and classification, medical image segmentation techniques, and intracerebral and subarachnoid hemorrhage research. Additional areas of their academic interest cover acute ischemic stroke management, COVID-19 diagnosis using artificial intelligence, domain adaptation and few-shot learning, and advanced neuroimaging techniques and applications.

Joaquim Salvi has published in several peer-reviewed venues, with multiple papers appearing in the SSRN Electronic Journal. Their work also appears in Frontiers in Neuroscience, Computerized Medical Imaging and Graphics, Machine Vision and Applications, and Applied Sciences.

Frequent collaborators include Arnau Oliver, Xavier Lladó, Valeriia Abramova, Sergi Valverde, and Albert Clèrigues, illustrating a collaborative research network across various specialties within medical imaging and AI applications.

Notable recent publications by Joaquim Salvi include:

  • Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation, 2021, Frontiers in Neuroscience
  • Minimizing the Effect of White Matter Lesions on Deep Learning Based Tissue Segmentation for Brain Volumetry, 2022, Computerized Medical Imaging and Graphics
  • Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA, 2023, PubMed
  • Motion-region Annotation for Complex Videos via Label Propagation Across Occluders, 2022, Machine Vision and Applications
  • An End-to-End Deep Learning Framework for Predicting Hematoma Expansion in Hemorrhagic Stroke Patients from CT Images, 2024, Applied Sciences

Their publication record reflects integration of advanced computational methods such as deep learning and domain adaptation in addressing clinical challenges related to brain imaging and diagnosis. The collaboration with specialists in artificial intelligence and radiology emphasizes the interdisciplinary nature of their research.

Best Publications

  • Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

    Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer

  • Pattern codification strategies in structured light systems

    Joaquim Salvi;Jordi Pagès;Joan Batlle

  • A state of the art in structured light patterns for surface profilometry

    Joaquim Salvi;Sergio Fernandez;Tomislav Pribanic;Xavier Llado

  • A review of recent range image registration methods with accuracy evaluation

    Joaquim Salvi;Carles Matabosch;David Fofi;Josep Forest

  • Review of CMOS image sensors

    M. Bigas;E. Cabruja;J. Salvi

  • A comparative review of camera calibrating methods with accuracy evaluation

    Joaquim Salvi;Xavier Armangué;Joan Batlle

  • RECENT PROGRESS IN CODED STRUCTURED LIGHT AS A TECHNIQUE TO SOLVE THE CORRESPONDENCE PROBLEM: A SURVEY

    Joan Batlle;El Mustapha Mouaddib;Joaquim Salvi

  • The SLAM problem: a survey

    Josep Aulinas;Yvan Petillot;Joaquim Salvi;Xavier Lladó

  • Overall view regarding fundamental matrix estimation

    Xavier Armangué;Joaquim Salvi

  • A robust-coded pattern projection for dynamic 3D scene measurement

    J. Salvi;J. Batlle;E. Mouaddib

  • Optimised De Bruijn patterns for one-shot shape acquisition

    Jordi Pagès;Joaquim Salvi;Christophe Collewet;Josep Forest

  • Overview of coded light projection techniques for automatic 3D profiling

    J. Pages;J. Salvi;R. Garcia;C. Matabosch

  • One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks

    Sergi Valverde;Mostafa Salem;Mariano Cabezas;Deborah Pareto

  • Fourier-based Registration for Robust Forward-looking Sonar Mosaicing in Low-visibility Underwater Environments

    Natília Hurtós;David Ribas;Xavier Cufí;Yvan Petillot

  • Recent progress in structured light in order to solve the correspondence problem in stereovision

    E. Mouaddib;J. Batlle;J. Salvi

  • Laser stripe peak detector for 3D scanners. A FIR filter approach

    J. Salvi;E. Cabruja;C. Pous

  • A Qualitative Review on 3D Coarse Registration Methods

    Yago Díez;Ferran Roure;Xavier Lladó;Joaquim Salvi

  • SLAM With Dynamic Targets via Single-Cluster PHD Filtering

    Chee Sing Lee;Daniel E. Clark;Joaquim Salvi

  • Efficient multiple phase shift patterns for dense 3D acquisition in structured light scanning

    Tomislav Pribanić;Saša Mrvoš;Joaquim Salvi

  • Fourier-based registrations for two-dimensional forward-looking sonar image mosaicing

    Natalia Hurtos;Xavier Cuf;Yvan Petillot;Joaquim Salvi

Frequent Co-Authors

Xavier Lladó
Xavier Lladó University of Girona
Yvan Petillot
Yvan Petillot Heriot-Watt University
Arnau Oliver
Arnau Oliver University of Girona
François Chaumette
François Chaumette University of Rennes
Rafael Garcia
Rafael Garcia Complutense University of Madrid
Tom Vercauteren
Tom Vercauteren King's College London
Arlindo L. Oliveira
Arlindo L. Oliveira University of Lisbon
Koen Van Leemput
Koen Van Leemput Harvard University
Pere Ridao
Pere Ridao University of Girona
Fabrice Meriaudeau
Fabrice Meriaudeau University of Franche-Comté

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