ShadiAlbarqouni

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Shadi Albarqouni

Shadi Albarqouni

  • Postdoctoral Research Associate

  • Email: shadi.albarqouni [@] tum.de

  • Address:

Chair for Computer Aided Medical Procedures & Augmented Reality
Fakultät für Informatik
Technische Universität München
Boltzmannstr. 3
85748 Garching b. München

Hot Stuff

  • Our recent paper on X-ray In-Depth Decomposition: Revealing the latent structures got accepted at MICCAI 2017, Quebec, Canada.
  • Our recent paper on Semi-Supervised Learning for Fully Convolutional Networks got accepted at MICCAI 2017, Quebec, Canada.
  • Our IEEE-TMI paper, AggNet, is one of the top 50 popular papers at IEEEXplore (worldwide) sine May 2016!
  • List of Papers on Deep Learning for Medical Applications
  • Our recent X-ray PoseNet paper got accepted at IEEE WACV conference in Santa Rosa, CA, USA.

Short Curriculum Vitae

2017/ Postdoctoral Research Associate, CAMP - Technische Universität München (TUM), Munich, Germany
2013/16 Visiting Scholar, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
2010/12 Lecturer in Electrical and Computer Engineering Departments, The Islamic University of Gaza, Palestine
2007/12 Lecturer in Information Technology Department, University College of Applied Science, Palestine
2005/07 Teaching Assistant in Electrical Engineering Department, The Islamic University of Gaza, Palestine

Background

2013/16* Ph.D. Informatics,Chair for Computer Aided Medical Procedures (CAMP), Technical University Munich, Germany
under the supervision of Prof. Dr. Nassir Navab
2005/07 M.Sc. Electrical Engineering, The Islamic University of Gaza, Palestine
Thesis: Re-evaluation and re-design of stand alone PV solar lighting projects: Gaza Strip, Palestine
under the supervision of Prof. Dr. Mohammed T. Hussein
2001/05 B.Sc. Electrical Engineering, The Islamic University of Gaza, Palestine
Finished within 4 years instead of the regular period of 5 years.

Scholarship & Awards

  • Best Paper Award at MIAR Conference 2016, Bern, Switzerland
  • 3rd rank in MICCAI-AMIDA13 challenge for Automatic Models for Mitosis Detection in Breast Cancer Histology Images
  • Ph.D. Fellowship
  • Best Master Thesis Award in Faculty of Engineering, Jun. 2010, The Islamic University of Gaza, Palestine.
  • Arab Bank Scholarship, Sept. 2001-Sept. 2002, Gaza, Palestine.

Professional Activities

Reviewer

Organizer

Member

Student Projects

Feel free to contact me to any of the following projects. open: drop by and ask!.

Available
ProjectA New Computational Algorithm for Treatment Planning of Targeted Radionuclide Therapy
(Dr. Kuangyu Shi, Shadi Albarqouni, Prof. Dr. Nassir Navab)

Running
Master ThesisAutomatic Classification of Liver Tumor
(Shadi Albarqouni, Peter Noel, Prof. Dr. Nassir Navab)
Master ThesisMedical Image Synthesis using Generative Adversarial Networks (GANs)
(Shadi Albarqouni, Christoph Baur, Prof. Dr. Nassir Navab)
IDPMS Lesion Segmentation in multi-channel subtraction images
(Christoph Baur, Shadi Albarqouni, Prof. Dr. Nassir Navab)

Finished
Bachelor ThesisMeta-Learning of Regularization Parameters in X-ray Computed Tomography
(Shadi Albarqouni, Tobias Lasser)
Master ThesisWeakly-Supervised Anomaly Detection assisted by Attention Models
(Shadi Albarqouni, Diana Mateus, Prof. Nassir Navab)
DA/MA/BAA comparative study on unsupervised deep learning methods
(Shadi Albarqouni, Christoph Baur, Prof. Nassir Navab)
Master ThesisSiemens AG: Detection of Complex Stents in Live Fluoroscopic Images for Endovascular Aneurysm Repair
(Shadi Albarqouni, Stefanie Demirci, Prof. Dr. Nassir Navab)
Master ThesisSiemens AG: X-ray PoseNet - Recovering the Poses of Portable X-Ray Device with Deep Learning
(Shadi Albarqouni, Slobodan Ilic, Prof. Nassir Navab)
Master ThesisA Deep Learning Approach to Synthesize Virtual CT based on Transmission Scan in hybrid PET/MR
(Sailesh Conjeti, Kuangyu Shi, Shadi Albarqouni, Prof. Dr. Nassir Navab)
ProjectCreating Diagnostic Model for Assessing the Success of Treatment for Eye Melanoma
(Shadi Albarqouni, Prof. Nassir Navab)
Master ThesisRohde & Schwarz: Deep feature representation with auxiliary embedding
(Shadi Albarqouni, Athanasios Karamalis, Prof. Nassir Navab)
Bachelor ThesisComparative Study on CNN Initialization
(Shadi Albarqouni, Prof. Nassir Navab)
IDPCancer Matestasis detection in Lymph nodes
(Shadi Albarqouni, Stefanie Demirci, Prof. Nassir Navab)
ProjectDepth Estimation for Catheters from Single-View Interventional X-ray Imaging
(Shadi Albarqouni, Stefanie Demirci, Pascal Fallavollita, Prof. Nassir Navab)
Bachelor ThesisGamification in the Medical Context
(Shadi Albarqouni,Stefanie Demirci, Maximilian Baust, Prof. Nassir Navab)
IDPCrowdsourcing in the Medical Context
(Shadi Albarqouni,Stefanie Demirci, Prof. Nassir Navab)
Master ThesisDepth Recovery from Single-View Interventional X-ray Imaging
(Shadi Albarqouni, Stefanie Demirci, Lichao Wang, Prof. Nassir Navab)

Teaching

List of Publications

2017
B. Wiestler, C. Baur, P. Eichinger, , T. Zhang, V. Biberacher, C. Zimmer, , J. Kirschke, S. Albarqouni
Fully Automated Multiple Sclerosis lesion detection on multi-channel subtraction images through an integrated Computer Vision- Machine Learning pipeline
Clinical Neuroradiolgy (2017) 27:S1-S118 (bib)
A. Kazi, S. Albarqouni, A. Sanchez, C. Kirchhoff, P. Biberthaler, N. Navab, D. Mateus
Automatic Classification of Proximal Femur Fractures based on Attention Models
Proceedings of MICCAI Workshop on Machine Learning in Medical Imaging (MLMI), Quebec, Canada, September 2017 (bib)
J. Cardoso, T. Arbel, J. Lee, V. Cheplygina, S. Balocco, D. Mateus, G. Zahnd, L. Maier-Hein, S. Demirci, E. Granger, L. Duong, M. A. Carbonneau, S. Albarqouni, G. Carneiro
Intravascular imaging and computer assisted stenting, and large-scale annotation of biomedical data and expert label synthesis
MICCAI 2017 Workshops (bib)
S. Albarqouni, J. Fotouhi, N. Navab
X-ray In-Depth Decomposition: Revealing The Latent Structures
Accepted to Proceedings of the 20th International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Quebec, Canada, September 2017
A pre-print version is available online at arXiv.
(bib)
M. Bui, S. Albarqouni, M. Schrapp, N. Navab, S. Ilic
X-ray PoseNet: 6 DoF Pose Estimation for Mobile X-ray Devices
Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), Mar 24, 2017 - Mar 31, 2017, Santa Rosa, USA
The first two authors contribute equally to this paper.
(bib)
C. Baur, S. Albarqouni, N. Navab
Semi-Supervised Learning for Fully Convolutional Networks
Accepted to Proceedings of the 20th International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Quebec, Canada, September 2017
A pre-print version is available online at arXiv.
The first two authors contribute equally to this paper.
(bib)
2016
S. Albarqouni, S. Matl, M. Baust, N. Navab, S. Demirci
Playsourcing: A Novel Concept for Knowledge Creation in Biomedical Research
Proceedings of MICCAI Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, Athens, Greece, October 2016
The first two authors contribute equally to this paper.
(bib)
C. Baur, S. Albarqouni, S. Demirci, N. Navab, P. Fallavollita
CathNets: Detection and Single-View Depth Prediction of Catheter Electrodes
7th International Conference on Medical Imaging and Augmented Reality (MIAR), 24-26 August, 2016, Bern, Switzerland. (Best Paper Award) (bib)
S. Albarqouni, U. Konrad, L. Wang, N. Navab, S. Demirci
Single-View X-Ray Depth Recovery: Towards a Novel Concept for Image-Guided Interventions
International Journal of Computer Assisted Radiology and Surgery (IJCARS), 2016, June 2016, Volume 11, Issue 6, pp 873-880. (bib)
S. Albarqouni, C. Baur, F. Achilles, V. Belagiannis, S. Demirci, N. Navab
AggNet: Deep Learning from Crowds for Mitosis Detection in Breast Cancer Histology Images
IEEE Transactions on Medical Imaging (TMI), Special Issue on Deep Learning, vol. 35, no. 5, pp. 1313 - 1321, 2016.
The first two authors contribute equally to this paper.
(bib)
A. Vahadane, T. Peng, A. Sethi, S. Albarqouni, L. Wang, M. Baust, K. Steiger, A. M. Schlitter, I. Esposito, N. Navab
Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images
IEEE Transactions on Medical Imaging (TMI), vol. 35, no. 8, pp. 1962 - 1971, 2016. (bib)
2015
S. Albarqouni, M. Baust, S. Conjeti, A. Al-Amoudi, N. Navab
Multi-scale Graph-based Guided Filter for De-noising Cryo-Electron Tomographic Data
Proceedings of the British Machine Vision Conference (BMVC), pages 17.1-17.10. BMVA Press, September 2015 (bib)
A. Vahadane, T. Peng, S. Albarqouni, M. Baust, K. Steiger, A. M. Schlitter, A. Sethi, I. Esposito, N. Navab
Structure-Preserved Color Normalization for Histological Images
International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, New York, USA, April 2015 (bib)
2014
S. Albarqouni, T. Lasser, W. Alkhaldi, A. Al-Amoudi, N. Navab
Gradient Projection for Regularized Cryo-Electron Tomographic Reconstruction
Proceedings of MICCAI Workshop on Computational Methods for Molecular Imaging, Boston, USA, September 2014. (bib)

Internship Offers

Collaboration

  • Rohde & Schwarz GmbH? & Co. KG.
  • Siemens AG, Corporate Technology Research & Technology Center
  • Augenklinik der Universität München

Active Projects

Crowdsourcing for Healthcare

Crowdsourcing for Healthcare

Today's clinical procedures often generate a large amount of digital images requiring close inspection. Manual examination by physicians is time-consuming and machine learning in computer vision and pattern recognition is playing an increasing role in medical applications. In contrast to pure machine learning methods, crowdsourcing can be used for processing big data sets, utilising the collective brainpower of huge crowds. Since individuals in the crowd are usually no medical experts, preparation of medical data as well as an appropriate visualization to the user becomes indispensable. The concept of gamification typically allows for embedding non-game elements in a serious game environment, providing an incentive for persistent engagement to the crowd. Medical image analysis empowered by the masses is still rare and only a few applications successfully use the crowd for solving medical problems. The goal of this project is to bring the gamification and crowdsourcing to the Medical Imaging community.

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UsersForm
Title: M.Sc.
Circumference of your head (in cm):  
Firstname: Shadi
Middlename:  
Lastname: Albarqouni
Picture: 0d6e8f1.jpg
Birthday: 07.08.1984
Nationality: Palestine
Languages: English, German, Arab
Groups: Reconstruction, Medical Imaging, Molecular Imaging, Machine Learning for Medical Applications, Microscopic Image Analysis, Crowdsourcing
Expertise: Medical Imaging
Position: Scientific Staff
Status: Active
Emailbefore: Shadi.Albarqouni
Emailafter: tum.de
Room: MI 03.13.056
Telephone: +49 89 289 19405
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Thesistitle: Machine Learning for Biomedical Applications: From Crowdsourcing to Deep Learning
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