Karim Lekadir (0000-0002-9456-1612)

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Prof. Karim Lekadir is ICREA Research Professor and Director of the Intelligence Artificial in Medicine Lab at the Universitat de Barcelona. He received a PhD in Medical Image Computing from Imperial College London and was a postdoctoral researcher at Stanford University. His current research focuses on the development of new medical AI tools from complex biomedical data, such as medical imaging, clinical and environmental data. He is particularly interested in trustworthy AI and related methods such as domain adaptation, bias correction, uncertainty estimation and federated learning. He has been the Project Coordinator of several Horizon 2020 projects (euCanSHare, EarlyCause, EuCanImage, DataTools4Heart, RadioVal). In addition, he has been awarded a research grant from the European Research Council (ERC Consolidator Grant) to develop new AI approaches for accessible medical imaging in low-to-middle income countries. He is General Chair of MICCAI 2024, the first edition to take place in Africa.

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Universitat de Barcelona: Barcelona, Catalonia, ES

2024-01-01 to present | ICREA Research Professor (Mathematics & Computer Science)

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Karim Lekadir

https://orcid.org/0000-0002-9456-1612

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Employment (9)

Universitat de Barcelona: Barcelona, Catalonia, ES

Universitat de Barcelona: Barcelona, ES

Universitat de Barcelona: Barcelona, Catalunya, ES

Universitat Pompeu Fabra: Barcelona, Catalunya, ES

Stanford University: Stanford, CA, US

Universitat Pompeu Fabra: Barcelona, ES

Imperial College London: London, GB

CMRtools: London, GB

EvoBus GmbH, Daimler AG: Mannheim, DE

Education and qualifications (5)

Imperial College London: London, GB

Université de Montpellier II: Montpellier, FR

Rheinisch Westfälische Technische Hochschule (RWTH) Aachen: Aachen, DE

SupMéca Paris: Paris, FR

Université de Bourgogne: Dijon, FR

Funding (7)

EuCanImage: A European Cancer Image Platform Linked to Biological and Health Data for Next-Generation Artificial Intelligence and Precision Medicine in Oncology

LongITools: Dynamic longitudinal exposome trajectories in cardiovascular and metabolic non-communicable diseases

EarlyCause: Causative mechanisms & integrative models linking early-life-stress to psycho-cardio-metabolic multi-morbidity

euCanSHare: An EU-Canada joint infrastructure for next-generation multi-Study Heart research

Computer-based personalised medicine

DECARTS - Decision support system for arterial stenting

Multi-object and multi-resolution statistical modeling of anatomical shapes

Works (24)

Mitigating Overdiagnosis Bias in CNN-Based Alzheimer’s Disease Diagnosis for the Elderly

Segment Anything Model for fetal head-pubic symphysis segmentation in intrapartum ultrasound image analysis

FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Digital twins: reimagining the future of cardiovascular risk prediction and personalised care

Fetal Head and Pubic Symphysis Segmentation in Intrapartum Ultrasound Image Using a Dual-Path Boundary-Guided Residual Network

Low-cost predictive models of dementia risk using machine learning and exposome predictors

Enhancing Heart Failure Management: Integrating AI with Telemonitoring Systems for Advanced Alarm-Based Solutions

A deep learning solution to detect left ventricular structural abnormalities with chest X-rays: towards trustworthy AI in cardiology

Effects of Pre- and Postnatal Early-Life Stress on Internalizing, Adiposity, and Their Comorbidity

Auditing Unfair Biases in CNN-Based Diagnosis of Alzheimer’s Disease

Revisiting Skin Tone Fairness in Dermatological Lesion Classification

A Review of Evaluation Approaches for Explainable AI With Applications in Cardiology

A Review of Evaluation Approaches for Explainable AI With Applications in Cardiology

Predicting left ventricular hypertrophy from the 12-lead electrocardiogram in the UK Biobank imaging study using machine learning

Low-cost predictive models of dementia risk using machine learning and exposome predictors

Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging

Center Dropout: A Simple Method for Speed and Fairness in Federated Learning

Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge

A Structural Heart-Brain Axis Mediates the Association Between Cardiovascular Risk and Cognitive Function

Data sharing platforms: instruments to inform and shape science policy on data sharing?

Convolutional neural network MRI segmentation for fast and robust optimization of transcranial electrical current stimulation of the human brain

Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge

A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound

Statistical estimation of femur micro-architecture using optimal shape and density predictors

Peer review (2 reviews for 2 publications/grants)