no code implementations • 20 May 2025 • Jorge Fabila, Lidia Garrucho, Víctor M. Campello, Carlos Martín-Isla, Karim Lekadir
The study compared locally trained models with a federated model built across all institutions to evaluate FL's real-world feasibility.
no code implementations • 13 May 2025 • Meritxell Riera-Marin, Sikha O K, Julia Rodriguez-Comas, Matthias Stefan May, Zhaohong Pan, Xiang Zhou, Xiaokun Liang, Franciskus Xaverius Erick, Andrea Prenner, Cedric Hemon, Valentin Boussot, Jean-Louis Dillenseger, Jean-Claude Nunes, Abdul Qayyum, Moona Mazher, Steven A Niederer, Kaisar Kushibar, Carlos Martin-Isla, Petia Radeva, Karim Lekadir, Theodore Barfoot, Luis C. Garcia Peraza Herrera, Ben Glocker, Tom Vercauteren, Lucas Gago, Justin Englemann, Joy-Marie Kleiss, Anton Aubanell, Andreu Antolin, Javier Garcia-Lopez, Miguel A. Gonzalez Ballester, Adrian Galdran
Deep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these models requires addressing key challenges such as annotation variability, calibration, and uncertainty estimation.
no code implementations • 2 Apr 2025 • Isabella Cama, Alejandro Guzmán, Cristina Campi, Michele Piana, Karim Lekadir, Sara Garbarino, Oliver Díaz
Instead, they advocate for the use of the Intraclass Correlation Coefficient (ICC) as a measure of stability for feature selection.
1 code implementation • 4 Mar 2025 • Grzegorz Skorupko, Fotios Avgoustidis, Carlos Martín-Isla, Lidia Garrucho, Dimitri A. Kessler, Esmeralda Ruiz Pujadas, Oliver Díaz, Maciej Bobowicz, Katarzyna Gwoździewicz, Xavier Bargalló, Paulius Jaruševičius, Kaisar Kushibar, Karim Lekadir
Federated learning is one of the approaches to train a segmentation model in a decentralized manner that helps preserve patient privacy.
1 code implementation • 20 Dec 2024 • Jun Ma, Feifei Li, Sumin Kim, Reza Asakereh, Bao-Hiep Le, Dang-Khoa Nguyen-Vu, Alexander Pfefferle, Muxin Wei, Ruochen Gao, Donghang Lyu, Songxiao Yang, Lennart Purucker, Zdravko Marinov, Marius Staring, Haisheng Lu, Thuy Thanh Dao, Xincheng Ye, Zhi Li, Gianluca Brugnara, Philipp Vollmuth, Martha Foltyn-Dumitru, Jaeyoung Cho, Mustafa Ahmed Mahmutoglu, Martin Bendszus, Irada Pflüger, Aditya Rastogi, Dong Ni, Xin Yang, Guang-Quan Zhou, Kaini Wang, Nicholas Heller, Nikolaos Papanikolopoulos, Christopher Weight, Yubing Tong, Jayaram K Udupa, Cahill J. Patrick, Yaqi Wang, Yifan Zhang, Francisco Contijoch, Elliot McVeigh, Xin Ye, Shucheng He, Robert Haase, Thomas Pinetz, Alexander Radbruch, Inga Krause, Erich Kobler, Jian He, Yucheng Tang, Haichun Yang, Yuankai Huo, Gongning Luo, Kaisar Kushibar, Jandos Amankulov, Dias Toleshbayev, Amangeldi Mukhamejan, Jan Egger, Antonio Pepe, Christina Gsaxner, Gijs Luijten, Shohei Fujita, Tomohiro Kikuchi, Benedikt Wiestler, Jan S. Kirschke, Ezequiel de la Rosa, Federico Bolelli, Luca Lumetti, Costantino Grana, Kunpeng Xie, Guomin Wu, Behrus Puladi, Carlos Martín-Isla, Karim Lekadir, Victor M. Campello, Wei Shao, Wayne Brisbane, Hongxu Jiang, Hao Wei, Wu Yuan, Shuangle Li, Yuyin Zhou, Bo wang
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice.
3 code implementations • 2 Dec 2024 • Nicholas Konz, Richard Osuala, Preeti Verma, YuWen Chen, Hanxue Gu, Haoyu Dong, Yaqian Chen, Andrew Marshall, Lidia Garrucho, Kaisar Kushibar, Daniel M. Lang, Gene S. Kim, Lars J. Grimm, John M. Lewin, James S. Duncan, Julia A. Schnabel, Oliver Diaz, Karim Lekadir, Maciej A. Mazurowski
Currently, metrics used for this task either rely on the (potentially biased) choice of some downstream task, such as segmentation, or adopt task-independent perceptual metrics (e. g., Fr\'echet Inception Distance/FID) from natural imaging, which we show insufficiently capture anatomical features.
1 code implementation • 27 Sep 2024 • Richard Osuala, Smriti Joshi, Apostolia Tsirikoglou, Lidia Garrucho, Walter H. L. Pinaya, Daniel M. Lang, Julia A. Schnabel, Oliver Diaz, Karim Lekadir
This paper presents a method for virtual contrast enhancement in breast MRI, offering a promising non-invasive alternative to traditional contrast agent-based DCE-MRI acquisition.
no code implementations • 17 Sep 2024 • Jieyun Bai, ZiHao Zhou, Zhanhong Ou, Gregor Koehler, Raphael Stock, Klaus Maier-Hein, Marawan Elbatel, Robert Martí, Xiaomeng Li, Yaoyang Qiu, Panjie Gou, Gongping Chen, Lei Zhao, Jianxun Zhang, Yu Dai, Fangyijie Wang, Guénolé Silvestre, Kathleen Curran, Hongkun Sun, Jing Xu, Pengzhou Cai, Lu Jiang, Libin Lan, Dong Ni, Mei Zhong, Gaowen Chen, Víctor M. Campello, Yaosheng Lu, Karim Lekadir
This challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5, 101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions.
1 code implementation • 11 Sep 2024 • Jianmei Jiang, Huijin Wang, Jieyun Bai, Shun Long, Shuangping Chen, Victor M. Campello, Karim Lekadir
The teacher model further reinforces learning within this architecture through the imposition of consistency regularization constraints.
no code implementations • 30 Aug 2024 • Jorge Fabila, Víctor M. Campello, Carlos Martín-Isla, Johnes Obungoloch, Kinyera Leo, Amodoi Ronald, Karim Lekadir
Africa faces significant challenges in healthcare delivery due to limited infrastructure and access to advanced medical technologies.
1 code implementation • 17 Jul 2024 • Richard Osuala, Daniel M. Lang, Anneliese Riess, Georgios Kaissis, Zuzanna Szafranowska, Grzegorz Skorupko, Oliver Diaz, Julia A. Schnabel, Karim Lekadir
This work addresses these challenges exploring and quantifying the utility of privacy-preserving deep learning techniques, concretely, (i) differentially private stochastic gradient descent (DP-SGD) and (ii) fully synthetic training data generated by our proposed malignancy-conditioned generative adversarial network.
1 code implementation • 19 Jun 2024 • Lidia Garrucho, Kaisar Kushibar, Claire-Anne Reidel, Smriti Joshi, Richard Osuala, Apostolia Tsirikoglou, Maciej Bobowicz, Javier del Riego, Alessandro Catanese, Katarzyna Gwoździewicz, Maria-Laura Cosaka, Pasant M. Abo-Elhoda, Sara W. Tantawy, Shorouq S. Sakrana, Norhan O. Shawky-Abdelfatah, Amr Muhammad Abdo-Salem, Androniki Kozana, Eugen Divjak, Gordana Ivanac, Katerina Nikiforaki, Michail E. Klontzas, Rosa García-Dosdá, Meltem Gulsun-Akpinar, Oğuz Lafcı, Ritse Mann, Carlos Martín-Isla, Fred Prior, Kostas Marias, Martijn P. A. Starmans, Fredrik Strand, Oliver Díaz, Laura Igual, Karim Lekadir
Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations.
1 code implementation • 30 May 2024 • Marta Buetas Arcas, Richard Osuala, Karim Lekadir, Oliver Díaz
However, the success of AI applications in this domain is restricted by the quantity and quality of available data, posing challenges due to limited and costly data annotation procedures that often lead to annotation shifts.
1 code implementation • 28 Mar 2024 • Grzegorz Skorupko, Richard Osuala, Zuzanna Szafranowska, Kaisar Kushibar, Nay Aung, Steffen E Petersen, Karim Lekadir, Polyxeni Gkontra
Notably, we conduct all our experiments using a single, consumer-level GPU to highlight the feasibility of our approach within resource-constrained environments.
2 code implementations • 20 Mar 2024 • Richard Osuala, Daniel M. Lang, Preeti Verma, Smriti Joshi, Apostolia Tsirikoglou, Grzegorz Skorupko, Kaisar Kushibar, Lidia Garrucho, Walter H. L. Pinaya, Oliver Diaz, Julia A. Schnabel, Karim Lekadir
Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to localize tumors and observe their contrast kinetics, which is essential for cancer characterization and respective treatment decision-making.
1 code implementation • 17 Nov 2023 • Richard Osuala, Smriti Joshi, Apostolia Tsirikoglou, Lidia Garrucho, Walter H. L. Pinaya, Oliver Diaz, Karim Lekadir
Despite its benefits for tumour detection and treatment, the administration of contrast agents in dynamic contrast-enhanced MRI (DCE-MRI) is associated with a range of issues, including their invasiveness, bioaccumulation, and a risk of nephrogenic systemic fibrosis.
1 code implementation • 14 Nov 2023 • Adrià Casamitjana, Roser Sala-Llonch, Karim Lekadir, Juan Eugenio Iglesias
We present USLR, a computational framework for longitudinal registration of brain MRI scans to estimate nonlinear image trajectories that are smooth across time, unbiased to any timepoint, and robust to imaging artefacts.
1 code implementation • 18 Aug 2023 • Thorsten Kalb, Kaisar Kushibar, Celia Cintas, Karim Lekadir, Oliver Diaz, Richard Osuala
Addressing fairness in lesion classification from dermatological images is crucial due to variations in how skin diseases manifest across skin tones.
no code implementations • 11 Aug 2023 • Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah, Alejandro F Frangi, Alena Buyx, Anais Emelie, Andrea Lara, Antonio R Porras, An-Wen Chan, Arcadi Navarro, Ben Glocker, Benard O Botwe, Bishesh Khanal, Brigit Beger, Carol C Wu, Celia Cintas, Curtis P Langlotz, Daniel Rueckert, Deogratias Mzurikwao, Dimitrios I Fotiadis, Doszhan Zhussupov, Enzo Ferrante, Erik Meijering, Eva Weicken, Fabio A González, Folkert W Asselbergs, Fred Prior, Gabriel P Krestin, Gary Collins, Geletaw S Tegenaw, Georgios Kaissis, Gianluca Misuraca, Gianna Tsakou, Girish Dwivedi, Haridimos Kondylakis, Harsha Jayakody, Henry C Woodruf, Horst Joachim Mayer, Hugo JWL Aerts, Ian Walsh, Ioanna Chouvarda, Irène Buvat, Isabell Tributsch, Islem Rekik, James Duncan, Jayashree Kalpathy-Cramer, Jihad Zahir, Jinah Park, John Mongan, Judy W Gichoya, Julia A Schnabel, Kaisar Kushibar, Katrine Riklund, Kensaku MORI, Kostas Marias, Lameck M Amugongo, Lauren A Fromont, Lena Maier-Hein, Leonor Cerdá Alberich, Leticia Rittner, Lighton Phiri, Linda Marrakchi-Kacem, Lluís Donoso-Bach, Luis Martí-Bonmatí, M Jorge Cardoso, Maciej Bobowicz, Mahsa Shabani, Manolis Tsiknakis, Maria A Zuluaga, Maria Bielikova, Marie-Christine Fritzsche, Marina Camacho, Marius George Linguraru, Markus Wenzel, Marleen de Bruijne, Martin G Tolsgaard, Marzyeh Ghassemi, Md Ashrafuzzaman, Melanie Goisauf, Mohammad Yaqub, Mónica Cano Abadía, Mukhtar M E Mahmoud, Mustafa Elattar, Nicola Rieke, Nikolaos Papanikolaou, Noussair Lazrak, Oliver Díaz, Olivier Salvado, Oriol Pujol, Ousmane Sall, Pamela Guevara, Peter Gordebeke, Philippe Lambin, Pieta Brown, Purang Abolmaesumi, Qi Dou, Qinghua Lu, Richard Osuala, Rose Nakasi, S Kevin Zhou, Sandy Napel, Sara Colantonio, Shadi Albarqouni, Smriti Joshi, Stacy Carter, Stefan Klein, Steffen E Petersen, Susanna Aussó, Suyash Awate, Tammy Riklin Raviv, Tessa Cook, Tinashe E M Mutsvangwa, Wendy A Rogers, Wiro J Niessen, Xènia Puig-Bosch, Yi Zeng, Yunusa G Mohammed, Yves Saint James Aquino, Zohaib Salahuddin, Martijn P A Starmans
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
no code implementations • 3 May 2023 • Ahmed Salih, Zahra Raisi-Estabragh, Ilaria Boscolo Galazzo, Petia Radeva, Steffen E. Petersen, Gloria Menegaz, Karim Lekadir
eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form.
domain classification
Explainable artificial intelligence
+1
1 code implementation • 10 Nov 2022 • Vien Ngoc Dang, Anna Cascarano, Rosa H. Mulder, Charlotte Cecil, Maria A. Zuluaga, Jerónimo Hernández-González, Karim Lekadir
Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations.
1 code implementation • 28 Sep 2022 • Richard Osuala, Grzegorz Skorupko, Noussair Lazrak, Lidia Garrucho, Eloy García, Smriti Joshi, Socayna Jouide, Michael Rutherford, Fred Prior, Kaisar Kushibar, Oliver Diaz, Karim Lekadir
Synthetic data generated by generative models can enhance the performance and capabilities of data-hungry deep learning models in medical imaging.
no code implementations • 20 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, Eduard Gratacós, Elisenda Bonet, Elisenda Eixarch, 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.
1 code implementation • 20 Sep 2022 • Lidia Garrucho, Kaisar Kushibar, Richard Osuala, Oliver Diaz, Alessandro Catanese, Javier del Riego, Maciej Bobowicz, Fredrik Strand, Laura Igual, Karim Lekadir
Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection.
no code implementations • 16 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.
1 code implementation • 8 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.
no code implementations • 27 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.
no code implementations • 14 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.
no code implementations • 20 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.
no code implementations • 1 Aug 2021 • Zhendong Liu, Van Manh, Xin Yang, Xiaoqiong Huang, Karim Lekadir, Víctor Campello, Nishant Ravikumar, Alejandro F Frangi, Dong Ni
A style transfer model with style fusion is employed to generate the curriculum samples.
no code implementations • 20 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.
1 code implementation • 7 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).
1 code implementation • 22 Jan 2021 • Vien Ngoc Dang, Francesco Galati, Rosa Cortese, Giuseppe Di Giacomo, Viola Marconetto, Prateek Mathur, Karim Lekadir, Marco Lorenzi, Ferran Prados, Maria A. Zuluaga
First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image.
no code implementations • 21 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.
no code implementations • 22 Jun 2020 • Xiahai Zhuang, Jiahang Xu, Xinzhe Luo, Chen Chen, Cheng Ouyang, Daniel Rueckert, Victor M. Campello, Karim Lekadir, Sulaiman Vesal, Nishant Ravikumar, Yashu Liu, Gongning Luo, Jingkun Chen, Hongwei Li, Buntheng Ly, Maxime Sermesant, Holger Roth, Wentao Zhu, Jiexiang Wang, Xinghao Ding, Xinyue Wang, Sen yang, Lei LI
In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the segmentation of LGE CMR.
no code implementations • 25 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.
no code implementations • 3 Sep 2019 • Víctor M. Campello, Carlos Martín-Isla, Cristian Izquierdo, Steffen E. Petersen, Miguel A. González Ballester, Karim Lekadir
Second, a deep learning model is trained for segmentation with different combinations of original, augmented and synthetic sequences.