Search Results for author: Karim Lekadir

Found 17 papers, 6 papers with code

Generalisability of deep learning models in low-resource imaging settings: A fetal ultrasound study in 5 African countries

no code implementations20 Sep 2022 Carla Sendra-Balcells, Víctor M. Campello, Jordina Torrents-Barrena, Yahya Ali Ahmed, Mustafa Elattar, Benard Ohene Botwe, Pempho Nyangulu, William Stones, Mohammed Ammar, Lamya Nawal Benamer, Harriet Nalubega Kisembo, Senai Goitom Sereke, Sikolia Z. Wanyonyi, Marleen Temmerman, Kamil Mikolaj, Martin Grønnebæk Tolsgaard, Karim Lekadir

This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for usability of AI in countries with less resources.

Transfer Learning

Layer Ensembles: A Single-Pass Uncertainty Estimation in Deep Learning for Segmentation

no code implementations16 Mar 2022 Kaisar Kushibar, Víctor Manuel Campello, Lidia Garrucho Moras, Akis Linardos, Petia Radeva, Karim Lekadir

In this paper, we propose Layer Ensembles, a novel uncertainty estimation method that uses a single network and requires only a single pass to estimate predictive uncertainty of a network.

Image Segmentation Medical Image Segmentation +1

Sharing Generative Models Instead of Private Data: A Simulation Study on Mammography Patch Classification

1 code implementation8 Mar 2022 Zuzanna Szafranowska, Richard Osuala, Bennet Breier, Kaisar Kushibar, Karim Lekadir, Oliver Diaz

Our experiments demonstrate that shared GANs notably increase the performance of both transformer and convolutional classification models and highlight this approach as a viable alternative to inter-centre data sharing.

Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center study

no code implementations27 Jan 2022 Lidia Garrucho, Kaisar Kushibar, Socayna Jouide, Oliver Diaz, Laura Igual, Karim Lekadir

In this work, we explore the domain generalization of deep learning methods for mass detection in digital mammography and analyze in-depth the sources of domain shift in a large-scale multi-center setting.

Breast Cancer Detection Domain Generalization +1

Domain generalization in deep learning for contrast-enhanced imaging

no code implementations14 Oct 2021 Carla Sendra-Balcells, Víctor M. Campello, Carlos Martín-Isla, David Viladés, Martín L. Descalzo, Andrea Guala, José F. Rodríguez-Palomares, Karim Lekadir

This calls for new tools for generalizing single-domain, single-center deep learning models across new unseen domains and clinical centers in contrast-enhanced imaging.

Anatomy Data Augmentation +3

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging

no code implementations20 Sep 2021 Karim Lekadir, Richard Osuala, Catherine Gallin, Noussair Lazrak, Kaisar Kushibar, Gianna Tsakou, Susanna Aussó, Leonor Cerdá Alberich, Kostas Marias, Manolis Tsiknakis, Sara Colantonio, Nickolas Papanikolaou, Zohaib Salahuddin, Henry C Woodruff, Philippe Lambin, Luis Martí-Bonmatí

The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI solutions across the whole value chain of medical imaging, including image reconstruction, medical image segmentation, image-based diagnosis and treatment planning.

Fairness Image Reconstruction +3

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

no code implementations20 Jul 2021 Richard Osuala, Kaisar Kushibar, Lidia Garrucho, Akis Linardos, Zuzanna Szafranowska, Stefan Klein, Ben Glocker, Oliver Diaz, Karim Lekadir

Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges.

Image Generation Lesion Detection

Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease

1 code implementation7 Jul 2021 Akis Linardos, Kaisar Kushibar, Sean Walsh, Polyxeni Gkontra, Karim Lekadir

We present the first federated learning study on the modality of cardiovascular magnetic resonance (CMR) and use four centers derived from subsets of the M\&M and ACDC datasets, focusing on the diagnosis of hypertrophic cardiomyopathy (HCM).

Action Recognition Data Augmentation +2

A radiomics approach to analyze cardiac alterations in hypertension

no code implementations21 Jul 2020 Irem Cetin, Steffen E. Petersen, Sandy Napel, Oscar Camara, Miguel Angel Gonzalez Ballester, Karim Lekadir

Hypertension is a medical condition that is well-established as a risk factor for many major diseases.

A Radiomics Approach to Computer-Aided Diagnosis with Cardiac Cine-MRI

no code implementations25 Sep 2019 Irem Cetin, Gerard Sanroma, Steffen E. Petersen, Sandy Napel, Oscar Camara, Miguel-Angel Gonzalez Ballester, Karim Lekadir

In this paper, we present a new approach to identify CVDs from cine-MRI by estimating large pools of radiomic features (statistical, shape and textural features) encoding relevant changes in anatomical and image characteristics due to CVDs.

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