World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
5832
World Ranking
11619
National Ranking
732

Overview

Giovanni Montana is affiliated with the University of Warwick in the United Kingdom. Their research primarily spans the field of Computer Science, with a focus on several subfields including Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Control and Systems Engineering, and Computational Theory and Mathematics.

The scientist has explored diverse topics in their work, including Reinforcement Learning in Robotics, Robot Manipulation and Learning, Artificial Intelligence in Healthcare and Education, Topological and Geometric Data Analysis, Medical Imaging Techniques and Applications, Advanced Neuroimaging Techniques and Applications, and Global Cancer Incidence and Screening.

Frequent coauthors of Giovanni Montana include:

  • Yue Jin
  • Andrew J. Lawrence
  • Carmine M. Pariante
  • Mitul A. Mehta
  • Ana Rodríguez-Mateos

The scientist's recent papers cover a range of topics and were published in notable venues. These include:

  • "Evaluation of an AI Model to Assess Future Breast Cancer Risk," 2023, Radiology
  • "Mediators and moderators in the relationship between maternal childhood adversity and children's emotional and behavioural development: a systematic review and meta-analysis," 2022, Psychological Medicine
  • "A Persistent Homology-Based Topological Loss for CNN-Based Multiclass Segmentation of CMR," 2022, IEEE Transactions on Medical Imaging
  • "Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study," 2023, The Lancet Digital Health
  • "PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals," 2020, arXiv (Cornell University)

Publications by Giovanni Montana have appeared frequently in the following venues:

  • arXiv (Cornell University)
  • Machine Learning
  • Neuroscience Applied
  • Psychoneuroendocrinology
  • Radiology

Best Publications

  • Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker.

    James H. Cole;Rudra P. K. Poudel;Dimosthenis Tsagkrasoulis;Matthan W. A. Caan

  • Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks

    Adrien Payan;Giovanni Montana

  • Deep neural networks for anatomical brain segmentation

    Alexandre de Brebisson;Giovanni Montana

  • Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks

    Mauro Annarumma;Samuel J. Withey;Robert J. Bakewell;Emanuele Pesce

  • Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation

    Rudra P. K. Poudel;Pablo Lamata;Giovanni Montana

  • Discovering genetic associations with high-dimensional neuroimaging phenotypes: A sparse reduced-rank regression approach.

    Maria Vounou;Thomas E. Nichols;Giovanni Montana

  • Estimating time-varying brain connectivity networks from functional MRI time series.

    Ricardo Pio Monti;Peter Hellyer;David J. Sharp;Robert Leech

  • Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks.

    Petros Pavlos Ypsilantis;Musib Siddique;Hyon Mok Sohn;Andrew Davies

  • Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease

    Maria Vounou;Eva Janousova;Robin Wolz;Jason L. Stein

  • False positives in neuroimaging genetics using voxel-based morphometry data.

    Matt Silver;Giovanni Montana;Thomas E. Nichols

  • Learning to detect chest radiographs containing pulmonary lesions using visual attention networks

    Emanuele Pesce;Samuel Joseph Withey;Petros-Pavlos Ypsilantis;Robert Bakewell

  • Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression☆

    Matt J Silver;Eva Janoušová;Xue Hua;Paul M Thompson

  • Community Detection in Multiplex Networks using Locally Adaptive Random Walks

    Zhana Kuncheva;Giovanni Montana

  • The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI

    Romy Lorenz;Ricardo Pio Monti;Inês R. Violante;Christoforos Anagnostopoulos

  • HapSim: a simulation tool for generating haplotype data with pre-specified allele frequencies and LD coefficients

    Giovanni Montana

  • Heritability maps of human face morphology through large-scale automated three-dimensional phenotyping

    Dimosthenis Tsagkrasoulis;Pirro Hysi;Tim Spector;Giovanni Montana;Giovanni Montana

  • Subspace clustering of high-dimensional data: a predictive approach

    Brian Mcwilliams;Giovanni Montana

  • Improving coordination in small-scale multi-agent deep reinforcement learning through memory-driven communication

    Emanuele Pesce;Giovanni Montana

  • Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks

    Savelie Cornegruta;Robert Bakewell;Samuel Withey;Giovanni Montana

  • Flexible least squares for temporal data mining and statistical arbitrage

    Giovanni Montana;Kostas Triantafyllopoulos;Theodoros Tsagaris

  • Statistical methods in genetics

    Giovanni Montana

Frequent Co-Authors

Robert Leech
Robert Leech King's College London
Daniel Rueckert
Daniel Rueckert Technical University of Munich
Andrew P. King
Andrew P. King King's College London
David J. Sharp
David J. Sharp Imperial College London
Adam Hampshire
Adam Hampshire Imperial College London
Paul M. Matthews
Paul M. Matthews Imperial College London
Michael Levin
Michael Levin Tufts University
Paul Aljabar
Paul Aljabar King's College London
Tim D. Spector
Tim D. Spector King's College London
Neil Brockdorff
Neil Brockdorff University of Oxford

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