World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
11246
World Ranking
6111
National Ranking
65

Overview

John Aldo Lee is affiliated with the Université Catholique de Louvain in Belgium and has a focused research career primarily within the medical field, especially relating to radiation and imaging technologies.

Their work spans several main fields of study including Medicine, with substantial contributions to subfields such as Pulmonary and Respiratory Medicine, Radiation, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, and Artificial Intelligence.

The research topics John Aldo Lee covers incorporate:

  • Advanced Radiotherapy Techniques
  • Radiation Therapy and Dosimetry
  • Medical Imaging Techniques and Applications
  • Radiation Effects in Electronics
  • Radiomics and Machine Learning in Medical Imaging
  • Advanced MRI Techniques and Applications
  • Face and Expression Recognition

Frequently published in several scientific venues, their contributions appear notably in:

  • Radiotherapy and Oncology
  • International Journal of Particle Therapy
  • Physics in Medicine and Biology
  • arXiv (Cornell University)
  • Medical Physics

John Aldo Lee has collaborated extensively with several recurring coauthors, including Edmond Sterpin, Ana María Barragán Montero, Kevin Souris, S. Wuyckens, and Macarena Chocan Vera. These collaborations reflect sustained research relationships across multiple studies.

Recent representative papers include:

  • Artificial intelligence and machine learning for medical imaging: A technology review (2021, Physica Medica)
  • Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency (2022, Physics in Medicine and Biology)
  • Deep learning dose prediction for IMRT of esophageal cancer: The effect of data quality and quantity on model performance (2021, Physica Medica)
  • Domain adversarial networks and intensity-based data augmentation for male pelvic organ segmentation in cone beam CT (2021, Computers in Biology and Medicine)
  • Treatment planning in arc proton therapy: Comparison of several optimization problem statements and their corresponding solvers (2022, Computers in Biology and Medicine)

Best Publications

  • Nonlinear Dimensionality Reduction

    John A. Lee;Michel Verleysen

  • Universal Dependencies 2.2

    Joakim Nivre;Mitchell Abrams;Željko Agić;Lars Ahrenberg

  • A gradient-based method for segmenting FDG-PET images: methodology and validation

    Xavier Geets;John Aldo Lee;Anne Bol;Max Lonneux

  • Nonlinear dimensionality reduction

    Michel Verleysen;John Aldo Lee

  • Artificial intelligence and machine learning for medical imaging: A technology review.

    Ana Barragán-Montero;Umair Javaid;Gilmer Valdés;Dan Nguyen

  • Quality assessment of dimensionality reduction: Rank-based criteria

    John A. Lee;Michel Verleysen

  • Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

    Dominik Sacha;Leishi Zhang;Michael Sedlmair;John A. Lee

  • Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

    Eric Laloy;Romain Hérault;John Aldo Lee;Diederik Jacques

  • Universal Dependencies 2.1

    Joakim Nivre;Željko Agić;Lars Ahrenberg;Lene Antonsen

  • Comparison of 12 deformable registration strategies in adaptive radiation therapy for the treatment of head and neck tumors

    Pierre Castadot;John Aldo Lee;Adriane Parraga;Adriane Parraga;Xavier Geets

  • Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysis

    John Aldo Lee;Amaury Lendasse;Michel Verleysen

  • Adaptive biological image-guided IMRT with anatomic and functional imaging in pharyngo-laryngeal tumors: Impact on target volume delineation and dose distribution using helical tomotherapy

    Xavier Geets;Milan Tomsej;John Aldo Lee;Thierry Duprez

  • Universal Dependencies 2.3

    Joakim Nivre;Mitchell Abrams;Željko Agić;Lars Ahrenberg

  • Classification and evaluation strategies of auto‐segmentation approaches for PET: Report of AAPM task group No. 211

    Mathieu Hatt;John Aldo Lee;Charles R Schmidtlein;Issam El Naqa

  • What you see is what you can change

    Dominik Sacha;Michael Sedlmair;Leishi Zhang;John A. Lee

  • Segmentation of positron emission tomography images: Some recommendations for target delineation in radiation oncology.

    John Aldo Lee

  • A robust nonlinear projection method

    John Aldo Lee;Amaury Lendasse;Nicolas Donckers;Michel Verleysen

  • A robust non-linear projection method.

    John Aldo Lee;Amaury Lendasse;Nicolas Donckers;Michel Verleysen

  • Fast multipurpose Monte Carlo simulation for proton therapy using multi- and many-core CPU architectures

    Kevin Souris;John Aldo Lee;Edmond Sterpin

  • Curvilinear Distance Analysis versus Isomap

    John Aldo Lee;Amaury Lendasse;Michel Verleysen

  • Universal Dependencies 2.7

    Daniel Zeman;Joakim Nivre;Mitchell Abrams;Elia Ackermann

Frequent Co-Authors

Michel Verleysen
Michel Verleysen Université Catholique de Louvain
Benoît Macq
Benoît Macq Université Catholique de Louvain
Amaury Lendasse
Amaury Lendasse University of Houston
Nizar Habash
Nizar Habash New York University Abu Dhabi
Barbara Plank
Barbara Plank Ludwig-Maximilians-Universität München
Samuel R. Bowman
Samuel R. Bowman New York University
Sampo Pyysalo
Sampo Pyysalo University of Turku
Jan Hajič
Jan Hajič Charles University
Filip Ginter
Filip Ginter University of Turku
Joakim Nivre
Joakim Nivre Uppsala University

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