AneesKazi

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

THIS WEBPAGE IS DEPRECATED - please visit our new website

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

  • *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. Jiménez-Sánchez, 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. Jiménez-Sánchez, 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)


UsersForm
Title: -none-
Circumference of your head (in cm):  
Firstname: Anees
Middlename:  
Lastname: Kazi
Picture:  
Birthday: 18.08.1991
Nationality: India
Languages:  
Groups:  
Expertise:  
Position: Scientific Staff
Status: Active
Emailbefore: anees.kazi
Emailafter: tum.de
Room: MI 03.13.040
Telephone: +49 89 289 17082
Alumniactivity:  
Defensedate:  
Thesistitle:  
Alumnihomepage:  
Personalvideo01:  
Personalvideotext01:  
Personalvideopreview01:  
Personalvideo02:  
Personalvideotext02:  
Personalvideopreview02:  


Edit | Attach | Refresh | Diffs | More | Revision r1.20 - 11 Jun 2021 - 06:57 - AneesKazi

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif