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2020 – today
- 2024
[j20]Shadi Albarqouni, Christian F. Baumgartner, Qi Dou, Ender Konukoglu, Bjoern H. Menze, Archana Venkataraman:
Editorial for the Special Issue on the 2022 Medical Imaging with Deep Learning Conference. Medical Image Anal. 98: 103308 (2024)- [j19]Yiping Jiao
, Jeroen van der Laak, Shadi Albarqouni, Zhang Li, Tao Tan, Abhir Bhalerao, Shenghua Cheng, Jiabo Ma, John Pocock, Josien P. W. Pluim, Navid Alemi Koohbanani, Raja Muhammad Saad Bashir, Shan-E-Ahmed Raza, Sibo Liu, Simon Graham, Suzanne C. Wetstein, Syed Ali Khurram, Xiuli Liu, Nasir M. Rajpoot, Mitko Veta, Francesco Ciompi:
LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset. IEEE J. Biomed. Health Informatics 28(3): 1161-1172 (2024) - [c53]Francesca De Benetti, Yousef Yaganeh, Claus Belka, Stefanie Corradini, Nassir Navab, Christopher Kurz, Guillaume Landry, Shadi Albarqouni, Thomas Wendler:
CloverNet - Leveraging Planning Annotations for Enhanced Procedural MR Segmentation: An Application to Adaptive Radiation Therapy. CLIP@MICCAI 2024: 1-10 - [c52]Elyes Farjallah, Said El Shamieh, Razieh Rezaei, Philipp Herrmann, Sandrine H. Künzel, Frank G. Holz, Shadi Albarqouni:
Affordable Deep Learning for Diagnosing Inherited and Common Retinal Diseases via Color Fundus Photography. OMIA@MICCAI 2024: 83-93 - 2023
- [j18]Holger R. Roth, Nicola Rieke, Shadi Albarqouni, Quanzheng Li:
Guest Editorial Special Issue on Federated Learning for Medical Imaging: Enabling Collaborative Development of Robust AI Models. IEEE Trans. Medical Imaging 42(7): 1914-1919 (2023) - [j17]Agnieszka Tomczak, Slobodan Ilic, Gaby Marquardt, Thomas Engel, Nassir Navab, Shadi Albarqouni:
Digital Staining of White Blood Cells With Confidence Estimation. IEEE Trans. Medical Imaging 42(12): 3895-3906 (2023) - [c51]Duy M. H. Nguyen, Hoang Nguyen, Truong Thanh Nhat Mai, Tri Cao, Binh T. Nguyen, Nhat Ho, Paul Swoboda, Shadi Albarqouni, Pengtao Xie, Daniel Sonntag:
Joint Self-Supervised Image-Volume Representation Learning with Intra-inter Contrastive Clustering. AAAI 2023: 14426-14435 - [c50]Duy M. H. Nguyen, Hoang Nguyen, Nghiem Tuong Diep, Tan Ngoc Pham, Tri Cao, Binh T. Nguyen, Paul Swoboda, Nhat Ho, Shadi Albarqouni, Pengtao Xie, Daniel Sonntag, Mathias Niepert:
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching. NeurIPS 2023 - [e10]M. Emre Celebi, Md Sirajus Salekin, Hyunwoo J. Kim, Shadi Albarqouni, Catarina Barata, Allan Halpern, Philipp Tschandl, Marc Combalia, Yuan Liu, Ghada Zamzmi, Joshua Levy, Huzefa Rangwala, Annika Reinke, Diya Wynn, Bennett A. Landman, Won-Ki Jeong, Yiqing Shen, Zhongying Deng, Spyridon Bakas, Xiaoxiao Li, Chen Qin, Nicola Rieke, Holger Roth, Daguang Xu:
Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops - ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8-12, 2023, Proceedings. Lecture Notes in Computer Science 14393, Springer 2023, ISBN 978-3-031-47400-2 [contents] - [i55]Yiping Jiao, Jeroen van der Laak, Shadi Albarqouni, Zhang Li, Tao Tan, Abhir Bhalerao, Jiabo Ma, Jiamei Sun, Johnathon Pocock, Josien P. W. Pluim, Navid Alemi Koohbanani, Raja Muhammad Saad Bashir, Shan E Ahmed Raza, Sibo Liu, Simon Graham, Suzanne C. Wetstein, Syed Ali Khurram, Thomas Watson, Nasir M. Rajpoot, Mitko Veta, Francesco Ciompi:
LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset. CoRR abs/2301.06304 (2023) - [i54]Duy M. H. Nguyen, Hoang Nguyen, Nghiem Tuong Diep, Tan Ngoc Pham, Tri Cao, Binh T. Nguyen, Paul Swoboda, Nhat Ho, Shadi Albarqouni, Pengtao Xie, Daniel Sonntag, Mathias Niepert:
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching. CoRR abs/2306.11925 (2023) - 2022
- [j16]Tariq M. Bdair, Benedikt Wiestler, Nassir Navab, Shadi Albarqouni:
ROAM: Random layer mixup for semi-supervised learning in medical images. IET Image Process. 16(10): 2593-2608 (2022) - [j15]Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, Shadi Albarqouni:
Federated disentangled representation learning for unsupervised brain anomaly detection. Nat. Mach. Intell. 4(8): 685-695 (2022) - [c49]Ahmed Ghorbel, Ahmed Aldahdooh, Shadi Albarqouni, Wassim Hamidouche:
Transformer Based Models for Unsupervised Anomaly Segmentation in Brain MR Images. BrainLes@MICCAI 2022: 25-44 - [c48]Salome Kazeminia, Ario Sadafi, Asya Makhro, Anna Bogdanova, Shadi Albarqouni, Carsten Marr:
Anomaly-Aware Multiple Instance Learning for Rare Anemia Disorder Classification. MICCAI (8) 2022: 341-350 - [c47]Agnieszka Tomczak, Aarushi Gupta, Slobodan Ilic, Nassir Navab, Shadi Albarqouni:
What Can We Learn About a Generated Image Corrupting Its Latent Representation? MICCAI (6) 2022: 505-515 - [c46]Raheleh Salehi, Ario Sadafi, Armin Gruber, Peter Lienemann, Nassir Navab, Shadi Albarqouni, Carsten Marr:
Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification. MICCAI (3) 2022: 739-748 - [c45]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 Telenczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux:
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. NeurIPS 2022 - [e9]Shadi Albarqouni, Spyridon Bakas, Sophia Bano, M. Jorge Cardoso, Bishesh Khanal, Bennett A. Landman, Xiaoxiao Li, Chen Qin, Islem Rekik, Nicola Rieke, Holger Roth, Debdoot Sheet, Daguang Xu:
Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health - Third MICCAI Workshop, DeCaF 2022, and Second MICCAI Workshop, FAIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, Proceedings. Lecture Notes in Computer Science 13573, Springer 2022, ISBN 978-3-031-18522-9 [contents] - [e8]Ender Konukoglu, Bjoern H. Menze, Archana Venkataraman, Christian F. Baumgartner, Qi Dou, Shadi Albarqouni:
International Conference on Medical Imaging with Deep Learning, MIDL 2022, 6-8 July 2022, Zurich, Switzerland. Proceedings of Machine Learning Research 172, PMLR 2022 [contents] - [i53]Tobias Bernecker, Annette Peters, Christopher L. Schlett, Fabian Bamberg, Fabian J. Theis, Daniel Rueckert, Jakob Weiß, Shadi Albarqouni:
FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation. CoRR abs/2205.11096 (2022) - [i52]Tariq M. Bdair, Hossam Abdelhamid, Nassir Navab, Shadi Albarqouni:
Virtual embeddings and self-consistency for self-supervised learning. CoRR abs/2206.06023 (2022) - [i51]Raheleh Salehi, Ario Sadafi, Armin Gruber, Peter Lienemann, Nassir Navab, Shadi Albarqouni, Carsten Marr:
Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification. CoRR abs/2207.00501 (2022) - [i50]Salome Kazeminia, Ario Sadafi, Asya Makhro, Anna Bogdanova, Shadi Albarqouni, Carsten Marr:
Anomaly-aware multiple instance learning for rare anemia disorder classification. CoRR abs/2207.01742 (2022) - [i49]Ahmed Ghorbel, Ahmed Aldahdooh, Shadi Albarqouni, Wassim Hamidouche:
Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Images. CoRR abs/2207.02059 (2022) - [i48]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 Telenczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux:
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. CoRR abs/2210.04620 (2022) - [i47]Agnieszka Tomczak, Aarushi Gupta, Slobodan Ilic, Nassir Navab, Shadi Albarqouni:
What can we learn about a generated image corrupting its latent representation? CoRR abs/2210.06257 (2022) - [i46]Duy M. H. Nguyen, Hoang Nguyen, Mai Thanh Nhat Truong, Tri Cao, Binh T. Nguyen, Nhat Ho, Paul Swoboda, Shadi Albarqouni, Pengtao Xie, Daniel Sonntag:
Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering. CoRR abs/2212.01893 (2022) - 2021
- [j14]Christoph Baur, Stefan Denner, Benedikt Wiestler, Nassir Navab, Shadi Albarqouni:
Autoencoders for unsupervised anomaly segmentation in brain MR images: A comparative study. Medical Image Anal. 69: 101952 (2021) - [j13]Amal Lahiani, Irina Klaman, Nassir Navab, Shadi Albarqouni, Eldad Klaiman:
Seamless Virtual Whole Slide Image Synthesis and Validation Using Perceptual Embedding Consistency. IEEE J. Biomed. Health Informatics 25(2): 403-411 (2021) - [j12]Agnieszka Tomczak, Slobodan Ilic, Gaby Marquardt, Thomas Engel, Frank Forster, Nassir Navab, Shadi Albarqouni:
Multi-Task Multi-Domain Learning for Digital Staining and Classification of Leukocytes. IEEE Trans. Medical Imaging 40(10): 2897-2910 (2021) - [c44]Ahmed Ayyad, Yuchen Li, Raden Muaz, Shadi Albarqouni, Mohamed Elhoseiny:
Semi-Supervised Few-Shot Learning with Prototypical Random Walks. MetaDL@AAAI 2021: 45-57 - [c43]Tetiana Klymenko, Seong Tae Kim, Kirsten Lauber, Christopher Kurz, Guillaume Landry, Nassir Navab, Shadi Albarqouni:
Butterfly-Net: Spatial-Temporal Architecture For Medical Image Segmentation. ISBI 2021: 616-620 - [c42]Ario Sadafi, Lucía María Moya Sans, Asya Makhro, Leonid Livshits, Nassir Navab, Anna Bogdanova, Shadi Albarqouni, Carsten Marr:
Fourier Transform of Percoll Gradients Boosts CNN Classification of Hereditary Hemolytic Anemias. ISBI 2021: 966-970 - [c41]Ario Sadafi, Asya Makhro, Leonid Livshits, Nassir Navab, Anna Bogdanova, Shadi Albarqouni, Carsten Marr:
Sickle Cell Disease Severity Prediction from Percoll Gradient Images Using Graph Convolutional Networks. DART/FAIR@MICCAI 2021: 216-225 - [c40]Tariq M. Bdair, Nassir Navab, Shadi Albarqouni:
FedPerl: Semi-supervised Peer Learning for Skin Lesion Classification. MICCAI (3) 2021: 336-346 - [c39]Matthäus Heer, Janis Postels, Xiaoran Chen, Ender Konukoglu, Shadi Albarqouni:
The OOD Blind Spot of Unsupervised Anomaly Detection. MIDL 2021: 286-300 - [e7]Cristina Oyarzun Laura, M. Jorge Cardoso, Michal Rosen-Zvi, Georgios Kaissis, Marius George Linguraru, Raj Shekhar, Stefan Wesarg, Marius Erdt, Klaus Drechsler, Yufei Chen, Shadi Albarqouni, Spyridon Bakas, Bennett A. Landman, Nicola Rieke, Holger Roth, Xiaoxiao Li, Daguang Xu, Maria Gabrani, Ender Konukoglu, Michal Guindy, Daniel Rueckert, Alexander Ziller, Dmitrii Usynin, Jonathan Passerat-Palmbach:
Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning - 10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings. Lecture Notes in Computer Science 12969, Springer 2021, ISBN 978-3-030-90873-7 [contents] - [e6]Shadi Albarqouni, Manuel Jorge Cardoso, Qi Dou, Konstantinos Kamnitsas, Bishesh Khanal, Islem Rekik, Nicola Rieke, Debdoot Sheet, Sotirios A. Tsaftaris, Daguang Xu, Ziyue Xu:
Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health - Third MICCAI Workshop, DART 2021, and First MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings. Lecture Notes in Computer Science 12968, Springer 2021, ISBN 978-3-030-87721-7 [contents] - [i45]Tariq M. Bdair, Nassir Navab, Shadi Albarqouni:
Peer Learning for Skin Lesion Classification. CoRR abs/2103.03703 (2021) - [i44]Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, Shadi Albarqouni:
FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation. CoRR abs/2103.03705 (2021) - [i43]Ario Sadafi, Lucía María Moya Sans, Asya Makhro, Leonid Livshits, Nassir Navab, Anna Bogdanova, Shadi Albarqouni, Carsten Marr:
Fourier Transform of Percoll Gradients Boosts CNN Classification of Hereditary Hemolytic Anemias. CoRR abs/2103.09671 (2021) - [i42]Sarthak Pati, Ujjwal Baid, Maximilian Zenk, Brandon Edwards, Micah J. 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 S. Marcus, Mikhail Milchenko, Arash Nazeri, Hassan M. Fathallah-Shaykh, Roland Wiest, András Jakab, Marc-André Weber, Abhishek Mahajan, Lena Maier-Hein, Jens Kleesiek, Bjoern H. Menze, Klaus H. Maier-Hein, Spyridon Bakas:
The Federated Tumor Segmentation (FeTS) Challenge. CoRR abs/2105.05874 (2021) - [i41]Ario Sadafi, Asya Makhro, Leonid Livshits, Nassir Navab, Anna Bogdanova, Shadi Albarqouni, Carsten Marr:
Sickle Cell Disease Severity Prediction from Percoll Gradient Images using Graph Convolutional Networks. CoRR abs/2109.05372 (2021) - 2020
- [j11]Salome Kazeminia, Christoph Baur, Arjan Kuijper, Bram van Ginneken, Nassir Navab, Shadi Albarqouni, Anirban Mukhopadhyay:
GANs for medical image analysis. Artif. Intell. Medicine 109: 101938 (2020) - [j10]Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni, Chlodwig Kirchhoff, Peter Biberthaler, Nassir Navab, Sonja Kirchhoff, Diana Mateus:
Precise proximal femur fracture classification for interactive training and surgical planning. Int. J. Comput. Assist. Radiol. Surg. 15(5): 847-857 (2020) - [j9]Mhd Hasan Sarhan, Shadi Albarqouni, Mehmet Yigitsoy, Nassir Navab, Abouzar Eslami:
Microaneurysms segmentation and diabetic retinopathy detection by learning discriminative representations. IET Image Process. 14(17): 4571-4578 (2020) - [j8]Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletarì, Holger R. Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N. Galtier, Bennett A. Landman, Klaus H. Maier-Hein, Sébastien Ourselin, Micah J. Sheller, Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust, M. Jorge Cardoso:
The future of digital health with federated learning. npj Digit. Medicine 3 (2020) - [j7]Mohammad Eslami, Solale Tabarestani, Shadi Albarqouni, Ehsan Adeli, Nassir Navab, Malek Adjouadi:
Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography. IEEE Trans. Medical Imaging 39(7): 2553-2565 (2020) - [c38]Mai Bui, Tolga Birdal, Haowen Deng, Shadi Albarqouni, Leonidas J. Guibas, Slobodan Ilic, Nassir Navab:
6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference. ECCV (18) 2020: 139-157 - [c37]Mhd Hasan Sarhan, Nassir Navab, Abouzar Eslami, Shadi Albarqouni:
Fairness by Learning Orthogonal Disentangled Representations. ECCV (29) 2020: 746-761 - [c36]Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab:
Bayesian Skip-Autoencoders for Unsupervised Hyperintense Anomaly Detection in High Resolution Brain Mri. ISBI 2020: 1905-1909 - [c35]Mhd Hasan Sarhan, Nassir Navab, Abouzar Eslami, Shadi Albarqouni:
On the Fairness of Privacy-Preserving Representations in Medical Applications. DART/DCL@MICCAI 2020: 140-149 - [c34]Yousef Yeganeh, Azade Farshad, Nassir Navab, Shadi Albarqouni:
Inverse Distance Aggregation for Federated Learning with Non-IID Data. DART/DCL@MICCAI 2020: 150-159 - [c33]Ario Sadafi, Asya Makhro, Anna Bogdanova, Nassir Navab, Tingying Peng, Shadi Albarqouni, Carsten Marr:
Attention Based Multiple Instance Learning for Classification of Blood Cell Disorders. MICCAI (5) 2020: 246-256 - [c32]Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab:
Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI. MICCAI (4) 2020: 552-561 - [c31]Arianne Tran, Jakob Weiss, Shadi Albarqouni, Shahrooz Faghih Roohi, Nassir Navab:
Retinal Layer Segmentation Reformulated as OCT Language Processing. MICCAI (5) 2020: 694-703 - [c30]Christoph Baur, Robert Graf, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab:
SteGANomaly: Inhibiting CycleGAN Steganography for Unsupervised Anomaly Detection in Brain MRI. MICCAI (2) 2020: 718-727 - [c29]Roger D. Soberanis-Mukul, Nassir Navab, Shadi Albarqouni:
Uncertainty-based Graph Convolutional Networks for Organ Segmentation Refinement. MIDL 2020: 755-769 - [e5]Shadi Albarqouni, Spyridon Bakas, Konstantinos Kamnitsas, M. Jorge Cardoso, Bennett A. Landman, Wenqi Li, Fausto Milletari, Nicola Rieke, Holger Roth, Daguang Xu, Ziyue Xu:
Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning - Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings. Lecture Notes in Computer Science 12444, Springer 2020, ISBN 978-3-030-60547-6 [contents] - [i40]Roger D. Soberanis-Mukul, Maxime Kayser, Anna-Maria Zvereva, Peter Klare, Nassir Navab, Shadi Albarqouni:
A learning without forgetting approach to incorporate artifact knowledge in polyp localization tasks. CoRR abs/2002.02883 (2020) - [i39]Mhd Hasan Sarhan, Nassir Navab, Abouzar Eslami, Shadi Albarqouni:
Fairness by Learning Orthogonal Disentangled Representations. CoRR abs/2003.05707 (2020) - [i38]Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N. Galtier, Bennett A. Landman, Klaus H. Maier-Hein, Sébastien Ourselin, Micah J. Sheller, Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust, M. Jorge Cardoso:
The Future of Digital Health with Federated Learning. CoRR abs/2003.08119 (2020) - [i37]Tariq M. Bdair, Nassir Navab, Shadi Albarqouni:
ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging. CoRR abs/2003.09439 (2020) - [i36]Christoph Baur, Stefan Denner, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab:
Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study. CoRR abs/2004.03271 (2020) - [i35]Mai Bui, Tolga Birdal, Haowen Deng, Shadi Albarqouni, Leonidas J. Guibas, Slobodan Ilic, Nassir Navab:
6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference. CoRR abs/2004.04807 (2020) - [i34]Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab:
Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI. CoRR abs/2006.12852 (2020) - [i33]Ario Sadafi, Asya Makhro, Anna Bogdanova, Nassir Navab, Tingying Peng, Shadi Albarqouni, Carsten Marr:
Attention based Multiple Instance Learning for Classification of Blood Cell Disorders. CoRR abs/2007.11641 (2020) - [i32]Yousef Yeganeh, Azade Farshad, Nassir Navab, Shadi Albarqouni:
Inverse Distance Aggregation for Federated Learning with Non-IID Data. CoRR abs/2008.07665 (2020) - [i31]Roger D. Soberanis-Mukul, Shadi Albarqouni, Nassir Navab:
Polyp-artifact relationship analysis using graph inductive learned representations. CoRR abs/2009.07109 (2020) - [i30]Roger D. Soberanis-Mukul, Nassir Navab, Shadi Albarqouni:
An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation. CoRR abs/2012.03352 (2020)
2010 – 2019
- 2019
- [j6]Sai Gokul Hariharan, Christian Kaethner, Norbert Strobel, Markus Kowarschik, Julie DiNitto, Shadi Albarqouni, Rebecca Fahrig, Nassir Navab:
Preliminary results of DSA denoising based on a weighted low-rank approach using an advanced neurovascular replication system. Int. J. Comput. Assist. Radiol. Surg. 14(7): 1117-1126 (2019) - [c28]Amal Lahiani, Jacob Gildenblat, Irina Klaman, Shadi Albarqouni, Nassir Navab, Eldad Klaiman:
Virtualization of Tissue Staining in Digital Pathology Using an Unsupervised Deep Learning Approach. ECDP 2019: 47-55 - [c27]Mai Bui, Christoph Baur, Nassir Navab, Slobodan Ilic, Shadi Albarqouni:
Adversarial Networks for Camera Pose Regression and Refinement. ICCV Workshops 2019: 3778-3787 - [c26]Anees Kazi, Shayan Shekarforoush, S. Arvind Krishna, Hendrik Burwinkel, Gerome Vivar, Karsten Kortüm, Seyed-Ahmad Ahmadi, Shadi Albarqouni, Nassir Navab:
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction. IPMI 2019: 73-85 - [c25]M. Tarek Shaban, Christoph Baur, Nassir Navab, Shadi Albarqouni:
Staingan: Stain Style Transfer for Digital Histological Images. ISBI 2019: 953-956 - [c24]Anees Kazi, S. Arvind Krishna, Shayan Shekarforoush, Karsten U. Kortuem, Shadi Albarqouni, Nassir Navab:
Self-Attention Equipped Graph Convolutions for Disease Prediction. ISBI 2019: 1896-1899 - [c23]Mhd Hasan Sarhan, Abouzar Eslami, Nassir Navab, Shadi Albarqouni:
Learning Interpretable Disentangled Representations Using Adversarial VAEs. DART/MIL3ID@MICCAI 2019: 37-44 - [c22]Anees Kazi, Shayan Shekarforoush, S. Arvind Krishna, Hendrik Burwinkel, Gerome Vivar, Benedikt Wiestler, Karsten Kortüm, Seyed-Ahmad Ahmadi, Shadi Albarqouni, Nassir Navab:
Graph Convolution Based Attention Model for Personalized Disease Prediction. MICCAI (4) 2019: 122-130 - [c21]Mhd Hasan Sarhan, Shadi Albarqouni, Mehmet Yigitsoy, Nassir Navab, Abouzar Eslami:
Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss. MICCAI (1) 2019: 174-182 - [c20]Sai Gokul Hariharan, Christian Kaethner, Norbert Strobel, Markus Kowarschik, Shadi Albarqouni, Rebecca Fahrig, Nassir Navab:
Learning-Based X-Ray Image Denoising Utilizing Model-Based Image Simulations. MICCAI (6) 2019: 549-557 - [c19]Amal Lahiani, Nassir Navab, Shadi Albarqouni, Eldad Klaiman:
Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer. MICCAI (1) 2019: 568-576 - [c18]Hendrik Burwinkel, Anees Kazi, Gerome Vivar, Shadi Albarqouni, Guillaume Zahnd, Nassir Navab, Seyed-Ahmad Ahmadi:
Adaptive Image-Feature Learning for Disease Classification Using Inductive Graph Networks. MICCAI (6) 2019: 640-648 - [c17]Ashkan Khakzar, Shadi Albarqouni, Nassir Navab:
Learning Interpretable Features via Adversarially Robust Optimization. MICCAI (6) 2019: 793-800 - [c16]Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab:
Fusing Unsupervised and Supervised Deep Learning for White Matter Lesion Segmentation. MIDL 2019: 63-72 - [e4]Hongen Liao, Simone Balocco, Guijin Wang, Feng Zhang, Yongpan Liu, Zijian Ding, Luc Duong, Renzo Phellan, Guillaume Zahnd, Katharina Breininger, Shadi Albarqouni, Stefano Moriconi, Su-Lin Lee, Stefanie Demirci:
Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting - First International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings. Lecture Notes in Computer Science 11794, Springer 2019, ISBN 978-3-030-33326-3 [contents] - [e3]Qian Wang, Fausto Milletari, Hien Van Nguyen, Shadi Albarqouni, M. Jorge Cardoso, Nicola Rieke, Ziyue Xu, Konstantinos Kamnitsas, Vishal Patel, Badri Roysam, Steve B. Jiang, S. Kevin Zhou, Khoa Luu, Ngan Le:
Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data - First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings. Lecture Notes in Computer Science 11795, Springer 2019, ISBN 978-3-030-33390-4 [contents] - [i29]Bodo Kaiser, Shadi Albarqouni:
MRI to CT Translation with GANs. CoRR abs/1901.05259 (2019) - [i28]Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni, Chlodwig Kirchhoff, Peter Biberthaler, Nassir Navab, Diana Mateus, Sonja Kirchhoff:
Towards an Interactive and Interpretable CAD System to Support Proximal Femur Fracture Classification. CoRR abs/1902.01338 (2019) - [i27]Ahmed Ayyad, Nassir Navab, Mohamed Elhoseiny, Shadi Albarqouni:
Semi-Supervised Few-Shot Learning with Local and Global Consistency. CoRR abs/1903.02164 (2019) - [i26]Anees Kazi, Shayan Shekarforoush, S. Arvind Krishna, Hendrik Burwinkel, Gerome Vivar, Karsten U. Kortuem, Seyed-Ahmad Ahmadi, Shadi Albarqouni, Nassir Navab:
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction. CoRR abs/1903.04233 (2019) - [i25]Mai Bui, Christoph Baur, Nassir Navab, Slobodan Ilic, Shadi Albarqouni:
Adversarial Joint Image and Pose Distribution Learning for Camera Pose Regression and Refinement. CoRR abs/1903.06646 (2019) - [i24]Mhd Hasan Sarhan, Abouzar Eslami, Nassir Navab, Shadi Albarqouni:
Learning Interpretable Disentangled Representations using Adversarial VAEs. CoRR abs/1904.08491 (2019) - [i23]Mhd Hasan Sarhan, Shadi Albarqouni, Nassir Navab, Abouzar Eslami:
Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss. CoRR abs/1904.12732 (2019) - [i22]Hendrik Burwinkel, Anees Kazi, Gerome Vivar, Shadi Albarqouni, Guillaume Zahnd, Nassir Navab, Seyed-Ahmad Ahmadi:
Adaptive image-feature learning for disease classification using inductive graph networks. CoRR abs/1905.03036 (2019) - [i21]Ashkan Khakzar, Shadi Albarqouni, Nassir Navab:
Learning Interpretable Features via Adversarially Robust Optimization. CoRR abs/1905.03767 (2019) - [i20]Amal Lahiani, Nassir Navab, Shadi Albarqouni, Eldad Klaiman:
Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer. CoRR abs/1906.00617 (2019) - [i19]Roger D. Soberanis-Mukul, Shadi Albarqouni, Nassir Navab:
An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation. CoRR abs/1906.02191 (2019) - [i18]Mohammad Eslami, Solale Tabarestani, Shadi Albarqouni, Ehsan Adeli, Nassir Navab, Malek Adjouadi:
Image to Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography. CoRR abs/1906.10089 (2019) - [i17]Abhijeet Parida, Arianne Tran, Nassir Navab, Shadi Albarqouni:
Learn to Segment Organs with a Few Bounding Boxes. CoRR abs/1909.07809 (2019) - [i16]Agnieszka Tomczack, Nassir Navab, Shadi Albarqouni:
Learn to Estimate Labels Uncertainty for Quality Assurance. CoRR abs/1909.08058 (2019) - 2018
- [j5]Sai Gokul Hariharan, Norbert Strobel, Christian Kaethner, Markus Kowarschik, Stefanie Demirci, Shadi Albarqouni, Rebecca Fahrig, Nassir Navab:
A photon recycling approach to the denoising of ultra-low dose X-ray sequences. Int. J. Comput. Assist. Radiol. Surg. 13(6): 847-854 (2018) - [j4]Katharina Breininger, Shadi Albarqouni, Tanja Kurzendorfer, Marcus Pfister, Markus Kowarschik, Andreas K. Maier:
Intraoperative stent segmentation in X-ray fluoroscopy for endovascular aortic repair. Int. J. Comput. Assist. Radiol. Surg. 13(8): 1221-1231 (2018) - [c15]Mai Bui, Shadi Albarqouni, Slobodan Ilic, Nassir Navab:
Scene Coordinate and Correspondence Learning for Image-Based Localization. BMVC 2018: 3 - [c14]Mai Bui, Sergey Zakharov, Shadi Albarqouni, Slobodan Ilic, Nassir Navab:
When Regression Meets Manifold Learning for Object Recognition and Pose Estimation. ICRA 2018: 1-7 - [c13]Katharina Breininger, Tobias Würfl, Tanja Kurzendorfer, Shadi Albarqouni, Marcus Pfister, Markus Kowarschik, Nassir Navab, Andreas K. Maier:
Multiple Device Segmentation for Fluoroscopic Imaging Using Multi-task Learning. CVII-STENT/LABELS@MICCAI 2018: 19-27 - [c12]Amelia Jiménez-Sánchez, Shadi Albarqouni, Diana Mateus:
Capsule Networks Against Medical Imaging Data Challenges. CVII-STENT/LABELS@MICCAI 2018: 150-160 - [c11]Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab:
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images. BrainLes@MICCAI (1) 2018: 161-169 - [c10]Christoph Baur, Shadi Albarqouni, Nassir Navab:
Generating Highly Realistic Images of Skin Lesions with GANs. OR 2.0/CARE/CLIP/ISIC@MICCAI 2018: 260-267 - [c9]Markus A. Degel, Nassir Navab, Shadi Albarqouni:
Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound. MICCAI (4) 2018: 630-637 - [e2]Danail Stoyanov, Zeike Taylor, Simone Balocco, Raphael Sznitman, Anne L. Martel, Lena Maier-Hein, Luc Duong, Guillaume Zahnd, Stefanie Demirci, Shadi Albarqouni, Su-Lin Lee, Stefano Moriconi, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Eric Granger, Pierre Jannin:
Intravascular Imaging and Computer Assisted Stenting - and - Large-Scale Annotation of Biomedical Data and Expert Label Synthesis - 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings. Lecture Notes in Computer Science 11043, Springer 2018, ISBN 978-3-030-01363-9 [contents] - [i15]M. Tarek Shaban, Christoph Baur, Nassir Navab, Shadi Albarqouni:
StainGAN: Stain Style Transfer for Digital Histological Images. CoRR abs/1804.01601 (2018) - [i14]Christoph Baur, Shadi Albarqouni, Nassir Navab:
MelanoGANs: High Resolution Skin Lesion Synthesis with GANs. CoRR abs/1804.04338 (2018) - [i13]Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab:
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images. CoRR abs/1804.04488 (2018) - [i12]Anees Kazi, Shadi Albarqouni, Karsten U. Kortuem, Nassir Navab:
Multi Layered-Parallel Graph Convolutional Network (ML-PGCN) for Disease Prediction. CoRR abs/1804.10776 (2018) - [i11]Markus A. Degel, Nassir Navab, Shadi Albarqouni:
Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound. CoRR abs/1805.00357 (2018) - [i10]Mai Bui, Sergey Zakharov, Shadi Albarqouni, Slobodan Ilic, Nassir Navab:
When Regression Meets Manifold Learning for Object Recognition and Pose Estimation. CoRR abs/1805.06400 (2018) - [i9]Mai Bui, Shadi Albarqouni, Slobodan Ilic, Nassir Navab:
Scene Coordinate and Correspondence Learning for Image-Based Localization. CoRR abs/1805.08443 (2018) - [i8]Amelia Jiménez-Sánchez, Shadi Albarqouni, Diana Mateus:
Capsule Networks against Medical Imaging Data Challenges. CoRR abs/1807.07559 (2018) - [i7]Christoph Baur, Shadi Albarqouni, Nassir Navab:
Generating Highly Realistic Images of Skin Lesions with GANs. CoRR abs/1809.01410 (2018) - [i6]Salome Kazeminia, Christoph Baur, Arjan Kuijper, Bram van Ginneken, Nassir Navab, Shadi Albarqouni, Anirban Mukhopadhyay:
GANs for Medical Image Analysis. CoRR abs/1809.06222 (2018) - [i5]Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni, Sonja Kirchhoff, Alexandra Sträter, Peter Biberthaler, Diana Mateus, Nassir Navab:
Weakly-Supervised Localization and Classification of Proximal Femur Fractures. CoRR abs/1809.10692 (2018) - [i4]Amal Lahiani, Jacob Gildenblat, Irina Klaman, Shadi Albarqouni, Nassir Navab, Eldad Klaiman:
Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach. CoRR abs/1810.06415 (2018) - [i3]Anees Kazi, S. Arvind Krishna, Shayan Shekarforoush, Karsten U. Kortuem, Shadi Albarqouni, Nassir Navab:
Self-Attention Equipped Graph Convolutions for Disease Prediction. CoRR abs/1812.09954 (2018) - 2017
- [c8]Anees Kazi, Shadi Albarqouni, Amelia Jiménez-Sánchez, Sonja Kirchhoff, Peter Biberthaler, Nassir Navab, Diana Mateus:
Automatic Classification of Proximal Femur Fractures Based on Attention Models. MLMI@MICCAI 2017: 70-78 - [c7]Christoph Baur, Shadi Albarqouni, Nassir Navab:
Semi-supervised Deep Learning for Fully Convolutional Networks. MICCAI (3) 2017: 311-319 - [c6]Shadi Albarqouni, Javad Fotouhi, Nassir Navab:
X-Ray In-Depth Decomposition: Revealing the Latent Structures. MICCAI (3) 2017: 444-452 - [c5]Mai Bui, Shadi Albarqouni, Michael Schrapp, Nassir Navab, Slobodan Ilic:
X-Ray PoseNet: 6 DoF Pose Estimation for Mobile X-Ray Devices. WACV 2017: 1036-1044 - [e1]M. Jorge Cardoso, Tal Arbel, Su-Lin Lee, Veronika Cheplygina, Simone Balocco, Diana Mateus, Guillaume Zahnd, Lena Maier-Hein, Stefanie Demirci, Eric Granger, Luc Duong, Marc-André Carbonneau, Shadi Albarqouni, Gustavo Carneiro:
Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis - 6th Joint International Workshops, CVII-STENT 2017 and Second International Workshop, LABELS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 10-14, 2017, Proceedings. Lecture Notes in Computer Science 10552, Springer 2017, ISBN 978-3-319-67533-6 [contents] - [i2]Christoph Baur, Shadi Albarqouni, Nassir Navab:
Auxiliary Manifold Embedding for Fully Convolutional Networks. CoRR abs/1703.06000 (2017) - 2016
- [j3]Shadi Albarqouni, Ulrich Konrad, Lichao Wang, Nassir Navab, Stefanie Demirci:
Single-view X-ray depth recovery: toward a novel concept for image-guided interventions. Int. J. Comput. Assist. Radiol. Surg. 11(6): 873-880 (2016) - [j2]Shadi Albarqouni, Christoph Baur, Felix Achilles, Vasileios Belagiannis, Stefanie Demirci, Nassir Navab:
AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images. IEEE Trans. Medical Imaging 35(5): 1313-1321 (2016) - [j1]Abhishek Vahadane, Tingying Peng, Amit Sethi, Shadi Albarqouni, Lichao Wang, Maximilian Baust, Katja Steiger, Anna Melissa Schlitter, Irene Esposito, Nassir Navab:
Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images. IEEE Trans. Medical Imaging 35(8): 1962-1971 (2016) - [c4]Christoph Baur, Shadi Albarqouni, Stefanie Demirci, Nassir Navab, Pascal Fallavollita:
CathNets: Detection and Single-View Depth Prediction of Catheter Electrodes. MIAR 2016: 38-49 - [c3]Shadi Albarqouni, Stefan Matl, Maximilian Baust, Nassir Navab, Stefanie Demirci:
Playsourcing: A Novel Concept for Knowledge Creation in Biomedical Research. LABELS/DLMIA@MICCAI 2016: 269-277 - [i1]Shadi Albarqouni, Javad Fotouhi, Nassir Navab:
X-ray In-Depth Decomposition: Can Deep Learning Reveal The Latent Structures? CoRR abs/1612.06096 (2016) - 2015
- [c2]Shadi Albarqouni, Maximilian Baust, Sailesh Conjeti, Asharf Al-Amoudi, Nassir Navab:
Multi-scale Graph-based Guided Filter for De-noising Cryo-Electron Tomographic Data. BMVC 2015: 17.1-17.10 - [c1]Abhishek Vahadane, Tingying Peng, Shadi Albarqouni, Maximilian Baust, Katja Steiger, Anna Melissa Schlitter, Amit Sethi, Irene Esposito, Nassir Navab:
Structure-preserved color normalization for histological images. ISBI 2015: 1012-1015
Coauthor Index
[c53] [j17] [j16] [c47] [c46] [i52] [i51] [i47] [j14] [j13] [j12] [c43] [c42] [c41] [c40] [i45] [i43] [i41] [j11] [j10] [j9] [j7] [c38] [c37] [c36] [c35] [c34] [c33] [c32] [c31] [c30] [c29] [i40] [i39] [i37] [i36] [i35] [i34] [i33] [i32] [i31] [i30] [j6] [c28] [c27] [c26] [c25] [c24] [c23] [c22] [c21] [c20] [c19] [c18] [c17] [c16] [i28] [i27] [i26] [i25] [i24] [i23] [i22] [i21] [i20] [i19] [i18] [i17] [i16] [j5] [c15] [c14] [c13] [c11] [c10] [c9] [i15] [i14] [i13] [i12] [i11] [i10] [i9] [i7] [i6] [i5] [i4] [i3] [c8] [c7] [c6] [c5] [i2] [j3] [j2] [j1] [c4] [c3] [i1] [c2] [c1]
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