AneesKazi

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

Anees Kazi

Anees Kazi
PhD Student

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

  • On-Going - Ph.D. in Deep learning for Medical Image Analysis.
  • 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

  • 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

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

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

Professional Associations and Memberships

  • IEEE Student Member
  • MICCAI Student Board Memeber 2019

Reviewer

  • Medical Image Computing & Computer Assisted Intervention 2019
  • Medical Image Computing & Computer Assisted Intervention 2018

Publications

2019
A. Kazi, , , H. Burwinkel, G. Vivar, B. Wiestler, , 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 (pre-print version is available online at arXiv) (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, , , H. Burwinkel, G. Vivar, , 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. (bib)
A. Kazi, , , , 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)
2018
A. Kazi, , 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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Room: MI 03.13.040
Telephone: +49 89 289 17082
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