Leonard Wee

Assistant Professor of Clinical Data Science

University of Maastricht

Netherlands

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About

Leonard Wee is an Assistant Professor of Clinical Data Science at Maastricht University in the Netherlands. His research focuses on applying advanced machine learning techniques to healthcare challenges, particularly in the areas of lung cancer segmentation and prognosis. Recent publications include work on federated learning frameworks and patient engagement in healthcare analytics. He is dedicated to improving diagnostic and treatment strategies through innovative data-driven approaches.

Articles (19)

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2020

Collaborators (21)

Chenyang Shen

University of Texas Southwestern Medical Center

UNITED STATES

Leonard Wee

Research Interests

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About

Articles (19)

Advancing Privacy-Preserving Health Care Analytics and Implementation of the Personal Health Train: Federated Deep Learning Study

Prognostic modeling in head and neck cancer: deep learning or handcrafted radiomics?

Data-efficient federated semi-supervised learning framework via pseudo supervision refinement strategy for lung tumor segmentation

Label-efficient transformer-based framework with self-supervised strategies for heterogeneous lung tumor segmentation

Adolescent and young adult (AYA) patient involvement and engagement in European health care and research projects: expanding the scope of patient advocacy

Lung Cancer Detection Using Bayesian Networks: A Retrospective Development and Validation Study on a Danish Population of High‐Risk Individuals

Improving Diagnosis and Care for Patients With Sarcoma: Do Real-World General Practitioners Data and Prospective Data Collections Have a Place Next to Clinical Trials?

Actionability of Synthetic Data in a Heterogeneous and Rare Health Care Demographic: Adolescents and Young Adults With Cancer

A Bayesian Network Approach to Lung Cancer Screening: Assessing the Impact of Data Quantity, Quality, and the Combination of Data from Danish Electronic Health Records

Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology

Comparing the performance of a deep learning-based lung gross tumour volume segmentation algorithm before and after transfer learning in a new hospital

Artificial intelligence research in radiation oncology: a practical guide for the clinician on concepts and methods

Clinical evaluation of automated segmentation for body composition analysis on abdominal L3 CT slices in polytrauma patients

Artificial Intelligence in Radiation Therapy

Practitioners’ views on shared decision-making implementation: A qualitative study

Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers

Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients

External validation of nodal failure prediction models including radiomics in head and neck cancer

Sensitivity of radiomic features to inter-observer variability and image pre-processing in Apparent Diffusion Coefficient (ADC) maps of cervix cancer patients

Collaborators (21)

Chenyang Shen

Suraj Pai

Johan van Soest

Varsha Gouthamchand

Martijn Poeze

Jetty Overbeek

Florian van Daalen

Alexander P. Seiffert

Flora Lysen

Winette van der Graaf

Tim Olde Hartman

Hajar Hasannejadasl

Anshu Ankolekar

Rianne Fijten

Sneha Mithun

Joshi Hogenboom

Andre Dekker

Alberto Traverso

Jan A Ten Bosch

Cheryl Roumen

Dr. Ashish Kumar Jha

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