Anees Kazi

Anees Kazi
Senior Research Scientist

Chair for Computer Aided Medical Procedures & Augmented Reality
Fakultät für Informatik
Technische Universität München
Boltzmannstr. 3
85748 Garching b. München
Room: MI 03.13.060

Phone: +49 89 289 17082
Fax: +49 89 289 17059

E-Mail:
Skype ID: anees.kazi7

Education
Awards
Teaching Assistance
Active Research Projects
Research Interests
Professional Associations and Memberships
Reviewer
Publications

Education

*Ph.D. in Graph Deep Learning for Healthcare Applications.
Masters of Technology - (2016) in Medical Imaging and Informatics, Indian Institute of Technology, Kharagpur, INDIA
Bachelor of Engineering - with honors (2013) in Electronics and Telecommunication, Dr.Babasaheb Ambedkar Technological University, Lonere, INDIA

Awards

MICCAI Student Board Incentive 2020 - Awarded by MICCAI 2020.
Graduate Student Travel Award 2019 - Awarded by MICCAI 2019.
TUM Global Incentive Award 2019 - Awarded to collaborate with Dept. of Computing at Imperial College of London.
Scholarship from Freunde und F{\"o}rderer der Augenklinik, M{\"u}nchen, Germany Feb. 2017- Feb. 2020 - Awarded to pursue Ph.D. jointly at Technical University of Munich and Augenklinik, M{\"u}nchen by
Elsevier Medical Image Analysis Best Paper Award, MICCAI 2016 for the paper on Metric Hashing Forests (Second Author).
Deutscher Akademischer Austauschdienst (DAAD) (Bonn, GERMANY) Scholarship, Sep. 2015 - Mar. 2016 - Awarded to pursue Master's Thesis at Chair for Computer Aided Medical Procedures & Augmented Reality, Fakultät für Informatik, Technische Universität München.
Ministry of Human Resources and Development, Government of India Scholarship for pursuing graduate studies in Medical Imaging and Informatics after qualifying Graduate Aptitude Test in Engineering. 2014-2016
Best student award - Awarded by Sojar English School Barshi for all round performance. 2007

Teaching Assistance

Active Research Projects

Analysis of graph-based methods for deep learning - application of graph convolutional network to disease prediction in the multi-graph setting.
Automatic Classification of the femur and distal radius fracture - Developing deep learning based models for classification and detection of fracture. The main focus of this project is to explore the attention models to localize and classify the fractures in X-ray images.
Deep learning for Ophthalmology - Main focus of this project is developing deep learning method for retinal disease classification. We work on real data from Augen Klinik Munich.
Deep Learning for medical image analysis.

Research Interests

Geometric Deep Learning
Machine Learning: Deep Learning, Image Retrieval.
Modalities: OCT, Histology, X-Ray, MR.

Professional Associations and Memberships

MICCAI Student Board- President 2020
IEEE Student Member
MICCAI Student Board- Memeber 2019

Reviewer

NeurIPS^? 2020
Medical Image Computing & Computer Assisted Intervention 2020
Neurocomputing
IEEE Transactions on Medical Imaging
Medical Image Computing & Computer Assisted Intervention 2019
Medical Image Computing & Computer Assisted Intervention 2018

Publications

2020
	A. Kazi, L. Cosmo, N. Navab, M. Bronstein Differentiable Graph Module (DGM) for Graph Convolutional Networks ArXiv available at https://arxiv.org/pdf/2002.04999.pdf (bib)
	, A. Kazi, S. Albarqouni, , P. Biberthaler, N. Navab, , D. Mateus Precise proximal femur fracture classification for interactive training and surgical planning Int. J. Comput. Assist. Radiol. Surg. 2020, 15(5), pp.847-857. (bib)
	L. Cosmo, A. Kazi, A. Ahmadi, N. Navab, M. Bronstein Latent-Graph Learning for Disease Prediction 23nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Lima, Peru, 2020 (bib)
	G. Vivar, A. Kazi, H. Burwinkel, A. Zwergal, N. Navab, A. Ahmadi Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC) The original version is available online at arXiv. (bib)
2019
	A. Kazi, S. Shekarforoush, S. Krishna, H. Burwinkel, G. Vivar, B. Wiestler, K. Kortüm, A. Ahmadi, S. Albarqouni, N. Navab Graph convolution based attention model for personalized disease prediction 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Shenzhen, China, 2019 (bib)
	H. Burwinkel, A. Kazi, G. Vivar, S. Albarqouni, G. Zahnd, N. Navab, A. Ahmadi Adaptive image-feature learning for disease classification using inductive graph networks International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Shenzhen, China, 2019 (pre-print version is available online at arXiv) (bib)
	A. Kazi, S. Shekarforoush, S. Krishna, H. Burwinkel, G. Vivar, K. Kortüm, A. Ahmadi, S. Albarqouni, N. Navab InceptionGCN : Receptive Field Aware Graph Convolutional Network for Disease Prediction (Oral) Proceedings of International Conference on Information Processing in Medical Imaging (IPMI), Hong Kong, 2019 (bib)
	G. Vivar, H. Burwinkel, A. Kazi, A. Zwergal, N. Navab, A. Ahmadi Multi-modal Graph Fusion for Inductive Disease Classification in Incomplete Datasets The original version is available online at arXiv. (bib)
	A. Kazi, S. Krishna, S. Shekarforoush, K. Kortüm, S. Albarqouni, N. Navab Self-Attention Equipped Graph Convolutions for Disease Prediction (Oral) Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI), Venice, Italy A pre-print version is available online at arXiv. (bib)
	A. JimÃ©nez-SÃ¡nchez, A. Kazi, S. Albarqouni, C. Kirchhoff, , , D. Mateus, C. Kirchhoff Towards an Interactive and Interpretable CAD System to Support Proximal Femur Fracture Classification (pre-print version is available online at arXiv) (bib)
2018
	A. Kazi, A. JimÃ©nez-SÃ¡nchez, S. Albarqouni, C. Kirchhoff, , P. Biberthaler, D. Mateus, N. Navab Weakly-Supervised Localization and Classificationof Proximal Femur Fractures (pre-print version is available online at arXiv) (bib)
2017
	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)
	A. Kazi, S. Conjeti, A. Katouzian, N. Navab Coupled Manifold Learning for Retrieval Across Modalities In Computer Vision Workshop (ICCVW), 2017 IEEE International Conference on (pp. 1321-1328). IEEE (bib)
2016
	S. Conjeti, A. Katouzian, A. Kazi, S. Mesbah, D. Beymer, T.F. Syeda Mahmood, N. Navab Metric Hashing Forests Medical Image Analysis, Special Issue MICCAI 2015, Best Paper Award, 2016. (bib)

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Firstname:	Anees
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Lastname:	Kazi
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Birthday:	18.08.1991
Nationality:	India
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Position:	Scientific Staff
Status:	Active
Emailbefore:	anees.kazi
Emailafter:	tum.de
Room:	MI 03.13.040
Telephone:	+49 89 289 17082
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		Chair for Computer Aided Medical Procedures & Augmented Reality Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality
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