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, Hugo JWL Aerts, Ian Walsh, Ioanna Chouvarda, Irène Buvat, 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, Marius George Linguraru, Markus Wenzel, Marleen de Bruijne, Martin G Tolsgaard, Marzyeh Ghassemi, Md Ashrafuzzaman, Melanie Goisauf, Mohammad Yaqub, Mohammed Ammar, 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 • 16 Jan 2023 • Yiping Jiao, Jeroen van der Laak, Shadi Albarqouni, Zhang Li, Tao Tan, Abhir Bhalerao, Jiabo Ma, Jiamei Sun, Johnathan Pocock, Josien P. W. Pluim, Navid Alemi Koohbanani, Raja Muhammad Saad Bashir, Shan E Ahmed Raza, Sibo Liu, Simon Graham, Suzanne Wetstein, Syed Ali Khurram, Thomas Watson, Nasir Rajpoot, Mitko Veta, Francesco Ciompi
Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists.
no code implementations • 4 Dec 2022 • Duy M. H. Nguyen, Hoang Nguyen, Mai T. N. Truong, Tri Cao, Binh T. Nguyen, Nhat Ho, Paul Swoboda, Shadi Albarqouni, Pengtao Xie, Daniel Sonntag
Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data.
no code implementations • 12 Oct 2022 • Agnieszka Tomczak, Aarushi Gupta, Slobodan Ilic, Nassir Navab, Shadi Albarqouni
The purpose of this work is to investigate the hypothesis that we can predict image quality based on its latent representation in the GANs bottleneck.
1 code implementation • 10 Oct 2022 • Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Teleńczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux
In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.
1 code implementation • 5 Jul 2022 • Ahmed Ghorbel, Ahmed Aldahdooh, Shadi Albarqouni, Wassim Hamidouche
The quality of patient care associated with diagnostic radiology is proportionate to a physician workload.
1 code implementation • 4 Jul 2022 • Salome Kazeminia, Ario Sadafi, Asya Makhro, Anna Bogdanova, Shadi Albarqouni, Carsten Marr
Deep learning-based classification of rare anemia disorders is challenged by the lack of training data and instance-level annotations.
1 code implementation • 1 Jul 2022 • Raheleh Salehi, Ario Sadafi, Armin Gruber, Peter Lienemann, Nassir Navab, Shadi Albarqouni, Carsten Marr
Here, we propose a cross-domain adapted autoencoder to extract features in an unsupervised manner on three different datasets of single white blood cells scanned from peripheral blood smears.
no code implementations • 13 Jun 2022 • Tariq Bdair, Hossam Abdelhamid, Nassir Navab, Shadi Albarqouni
We validate TriMix on eight benchmark datasets consisting of natural and medical images with an improvement of 2. 71% and 0. 41% better than the second-best models for both data types.
no code implementations • 23 May 2022 • Tobias Bernecker, Annette Peters, Christopher L. Schlett, Fabian Bamberg, Fabian Theis, Daniel Rueckert, Jakob Weiß, Shadi Albarqouni
One of the most common methods for analyzing CT and MRI images for diagnosis and follow-up treatment is liver segmentation.
no code implementations • 11 Sep 2021 • Ario Sadafi, Asya Makhro, Leonid Livshits, Nassir Navab, Anna Bogdanova, Shadi Albarqouni, Carsten Marr
Sickle cell disease (SCD) is a severe genetic hemoglobin disorder that results in premature destruction of red blood cells.
2 code implementations • 12 May 2021 • Sarthak Pati, Ujjwal Baid, Maximilian Zenk, Brandon Edwards, Micah Sheller, G. Anthony Reina, Patrick Foley, Alexey Gruzdev, Jason Martin, Shadi Albarqouni, Yong Chen, Russell Taki Shinohara, Annika Reinke, David Zimmerer, John B. Freymann, Justin S. Kirby, Christos Davatzikos, Rivka R. Colen, Aikaterini Kotrotsou, Daniel Marcus, Mikhail Milchenko, Arash Nazer, Hassan Fathallah-Shaykh, Roland Wiest, Andras Jakab, Marc-Andre Weber, Abhishek Mahajan, Lena Maier-Hein, Jens Kleesiek, Bjoern Menze, Klaus Maier-Hein, Spyridon Bakas
The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge via federated learning from multiple geographically distinct institutions, while their data are always retained within each institution, and 2) the federated evaluation of the generalizability of brain tumor segmentation models "in the wild", i. e. on data from institutional distributions that were not part of the training datasets.
1 code implementation • 17 Mar 2021 • Ario Sadafi, Lucía María Moya Sans, Asya Makhro, Leonid Livshits, Nassir Navab, Anna Bogdanova, Shadi Albarqouni, Carsten Marr
Hereditary hemolytic anemias are genetic disorders that affect the shape and density of red blood cells.
no code implementations • 5 Mar 2021 • Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, Shadi Albarqouni
Further, we illustrate that FedDis learns a shape embedding that is orthogonal to the appearance and consistent under different intensity augmentations.
1 code implementation • 5 Mar 2021 • Tariq Bdair, Nassir Navab, Shadi Albarqouni
With few annotated data, FedPerl is on par with a state-of-the-art method in skin lesion classification in the standard setup while outperforming SSFLs and the baselines by 1. 8% and 15. 8%, respectively.
1 code implementation • 6 Dec 2020 • Roger D. Soberanis-Mukul, Nassir Navab, Shadi Albarqouni
In this context, we proposed a segmentation refinement method based on uncertainty analysis and graph convolutional networks.
no code implementations • 15 Sep 2020 • Roger D. Soberanis-Mukul, Shadi Albarqouni, Nassir Navab
In inference, we use this classifier to analyze a second graph, generated from artifact and polyp predictions given by region proposal networks.
no code implementations • 17 Aug 2020 • Yousef Yeganeh, Azade Farshad, Nassir Navab, Shadi Albarqouni
Federated learning (FL) has been a promising approach in the field of medical imaging in recent years.
1 code implementation • 22 Jul 2020 • Ario Sadafi, Asya Makhro, Anna Bogdanova, Nassir Navab, Tingying Peng, Shadi Albarqouni, Carsten Marr
In blood cell disorders, only a subset of all cells is morphologically altered and relevant for the diagnosis.
1 code implementation • 23 Jun 2020 • Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab
Brain pathologies can vary greatly in size and shape, ranging from few pixels (i. e. MS lesions) to large, space-occupying tumors.
2 code implementations • ECCV 2020 • Mai Bui, Tolga Birdal, Haowen Deng, Shadi Albarqouni, Leonidas Guibas, Slobodan Ilic, Nassir Navab
We present a multimodal camera relocalization framework that captures ambiguities and uncertainties with continuous mixture models defined on the manifold of camera poses.
1 code implementation • 7 Apr 2020 • Christoph Baur, Stefan Denner, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab
Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI.
1 code implementation • 20 Mar 2020 • Tariq Bdair, Benedikt Wiestler, Nassir Navab, Shadi Albarqouni
Medical image segmentation is one of the major challenges addressed by machine learning methods.
no code implementations • 18 Mar 2020 • Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N. Galtier, Bennett Landman, Klaus Maier-Hein, Sebastien Ourselin, Micah Sheller, Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust, M. Jorge Cardoso
Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems.
1 code implementation • ECCV 2020 • Mhd Hasan Sarhan, Nassir Navab, Abouzar Eslami, Shadi Albarqouni
We explicitly enforce the meaningful representation to be agnostic to sensitive information by entropy maximization.
1 code implementation • 7 Feb 2020 • Maxime Kayser, Roger D. Soberanis-Mukul, Anna-Maria Zvereva, Peter Klare, Nassir Navab, Shadi Albarqouni
We then investigated different strategies, such as a learning without forgetting framework, to leverage artifact knowledge to improve automated polyp detection.
no code implementations • 17 Sep 2019 • Agnieszka Tomczack, Nassir Navab, Shadi Albarqouni
Deep Learning sets the state-of-the-art in many challenging tasks showing outstanding performance in a broad range of applications.
no code implementations • 17 Sep 2019 • Abhijeet Parida, Arianne Tran, Nassir Navab, Shadi Albarqouni
Semantic segmentation is an import task in the medical field to identify the exact extent and orientation of significant structures like organs and pathology.
1 code implementation • 24 Jun 2019 • Mohammad Eslami, Solale Tabarestani, Shadi Albarqouni, Ehsan Adeli, Nassir Navab, Malek Adjouadi
Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widely-used for diagnosis, screening, and treatment follow up of diseases related to lungs and heart.
1 code implementation • MIDL 2019 • Roger D. Soberanis-Mukul, Nassir Navab, Shadi Albarqouni
In this context, we proposed a segmentation refinement method based on uncertainty analysis and graph convolutional networks.
no code implementations • 3 Jun 2019 • Amal Lahiani, Nassir Navab, Shadi Albarqouni, Eldad Klaiman
Recent work has shown Generative Adversarial Networks(GANs) can be used to create realistic images of virtually stained slide images in digital pathology with clinically validated interpretability.
no code implementations • 9 May 2019 • Ashkan Khakzar, Shadi Albarqouni, Nassir Navab
In this work, we propose a method for improving the feature interpretability of neural network classifiers.
no code implementations • 8 May 2019 • Hendrik Burwinkel, Anees Kazi, Gerome Vivar, Shadi Albarqouni, Guillaume Zahnd, Nassir Navab, Seyed-Ahmad Ahmadi
We propose a new network architecture that exploits an inductive end-to-end learning approach for disease classification, where filters from both the CNN and the graph are trained jointly.
no code implementations • 18 Apr 2019 • Mhd Hasan Sarhan, Shadi Albarqouni, Mehmet Yigitsoy, Nassir Navab, Abouzar Eslami
To enhance the discriminative power of the classification model, we incorporate triplet embedding loss with a selective sampling routine.
no code implementations • 17 Apr 2019 • Mhd Hasan Sarhan, Abouzar Eslami, Nassir Navab, Shadi Albarqouni
Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice.
no code implementations • 15 Mar 2019 • Mai Bui, Christoph Baur, Nassir Navab, Slobodan Ilic, Shadi Albarqouni
Despite recent advances on the topic of direct camera pose regression using neural networks, accurately estimating the camera pose of a single RGB image still remains a challenging task.
no code implementations • 11 Mar 2019 • Anees Kazi, Shayan shekarforoush, S. Arvind krishna, Hendrik Burwinkel, Gerome Vivar, Karsten Kortuem, Seyed-Ahmad Ahmadi, Shadi Albarqouni, Nassir Navab
Geometric deep learning provides a principled and versatile manner for the integration of imaging and non-imaging modalities in the medical domain.
1 code implementation • 6 Mar 2019 • Ahmed Ayyad, Yuchen Li, Nassir Navab, Shadi Albarqouni, Mohamed Elhoseiny
We develop a random walk semi-supervised loss that enables the network to learn representations that are compact and well-separated.
no code implementations • 4 Feb 2019 • Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni, Chlodwig Kirchhoff, Peter Biberthaler, Nassir Navab, Sonja Kirchhoff, Diana Mateus
We demonstrate the feasibility of a fully automatic computer-aided diagnosis (CAD) tool, based on deep learning, that localizes and classifies proximal femur fractures on X-ray images according to the AO classification.
3 code implementations • 16 Jan 2019 • Bodo Kaiser, Shadi Albarqouni
We present a detailed description and reference implementation of preprocessing steps necessary to prepare the public Retrospective Image Registration Evaluation (RIRE) dataset for the task of magnetic resonance imaging (MRI) to X-ray computed tomography (CT) translation.
no code implementations • 24 Dec 2018 • Anees Kazi, S. Arvind krishna, Shayan Shekarforoush, Karsten Kortuem, Shadi Albarqouni, Nassir Navab
A model capable of leveraging the individuality of each multi-modal data is required for better disease prediction.
no code implementations • 15 Oct 2018 • Amal Lahiani, Jacob Gildenblat, Irina Klaman, Shadi Albarqouni, Nassir Navab, Eldad Klaiman
Histopathological evaluation of tissue samples is a key practice in patient diagnosis and drug development, especially in oncology.
no code implementations • 27 Sep 2018 • Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni, Sonja Kirchhoff, Alexandra Sträter, Peter Biberthaler, Diana Mateus, Nassir Navab
In this paper, we target the problem of fracture classification from clinical X-Ray images towards an automated Computer Aided Diagnosis (CAD) system.
no code implementations • 13 Sep 2018 • Salome Kazeminia, Christoph Baur, Arjan Kuijper, Bram van Ginneken, Nassir Navab, Shadi Albarqouni, Anirban Mukhopadhyay
Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification.
no code implementations • 5 Sep 2018 • Christoph Baur, Shadi Albarqouni, Nassir Navab
As many other machine learning driven medical image analysis tasks, skin image analysis suffers from a chronic lack of labeled data and skewed class distributions, which poses problems for the training of robust and well-generalizing models.
1 code implementation • 19 Jul 2018 • Amelia Jiménez-Sánchez, Shadi Albarqouni, Diana Mateus
A key component to the success of deep learning is the availability of massive amounts of training data.
no code implementations • 22 May 2018 • Mai Bui, Shadi Albarqouni, Slobodan Ilic, Nassir Navab
Scene coordinate regression has become an essential part of current camera re-localization methods.
no code implementations • 16 May 2018 • Mai Bui, Sergey Zakharov, Shadi Albarqouni, Slobodan Ilic, Nassir Navab
By combining the strengths of manifold learning using triplet loss and pose regression, we could either estimate the pose directly reducing the complexity compared to NN search, or use learned descriptor for the NN descriptor matching.
no code implementations • 28 Apr 2018 • Anees Kazi, Shadi Albarqouni, Karsten Kortuem, Nassir Navab
Structural data from Electronic Health Records as complementary information to imaging data for disease prediction.
no code implementations • 20 Apr 2018 • Markus A. Degel, Nassir Navab, Shadi Albarqouni
Segmentation of the left atrium and deriving its size can help to predict and detect various cardiovascular conditions.
no code implementations • 12 Apr 2018 • Christoph Baur, Shadi Albarqouni, Nassir Navab
Generative Adversarial Networks (GANs) have been successfully used to synthesize realistically looking images of faces, scenery and even medical images.
1 code implementation • 12 Apr 2018 • Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab
Reliably modeling normality and differentiating abnormal appearances from normal cases is a very appealing approach for detecting pathologies in medical images.
1 code implementation • 4 Apr 2018 • M Tarek Shaban, Christoph Baur, Nassir Navab, Shadi Albarqouni
Digitized Histological diagnosis is in increasing demand.
1 code implementation • 17 Mar 2017 • Christoph Baur, Shadi Albarqouni, Nassir Navab
Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious.
no code implementations • 19 Dec 2016 • Shadi Albarqouni, Javad Fotouhi, Nassir Navab
X-ray radiography is the most readily available imaging modality and has a broad range of applications that spans from diagnosis to intra-operative guidance in cardiac, orthopedics, and trauma procedures.