MEDIA - Medical Image Analysis
About
Medical Image Analysis group was founded in 2015 with main focus on fundamentals of Machine Learning followed by Deep learning for medical imaging modalities such as histology, thoracic X-rays, X-ray with fractures, CTs, brain imaging. The most impactful works being V-Net CITE, Agg-Net CITE, Squeeze and Excite CITE. Currently, the Medical Image Analysis (MedIA) group focuses on top-level research on advanced machine learning and deep learning algorithms aiming to attend medical-image-related problems. Research done by MedIA includes (but is not limited to) generative models, multi-modal data analysis, geometric deep learning, semi, weakly, and self-supervised learning, meta-leaning, and federated learning. The group also organizes the Deep Learning for Medical Applications (DLMA) seminar and the Machine Learning in Medical Imaging (MLMI) practical course.
Contact Person / Group Coordination
Azade Farshad - azade.farshad@tum.de
Research Partner
TU
Klinikum Recht Der Isar
Imperial College London
Nvidia healthcare
Ulm University
Oxford (VGG group)
Helmholtz AI
Johns Hopkins
Harvard
Sharif
Kyung Hee University Seong Tae Kim
Google
Research Grants and Awards
BigPicture
MCML: Meta-learning & Interpretability
Episodic Semantic Scene Analysis: with CV team
DIVA
COVID
DAAD
DAAD: Doctoral Programmes in Germany
TUM-ICL JAD 2020
Project Members
- Anees Kazi
- Ashkan Khakzar
- Azade Farshad
- Mahsa Ghorbani
- Shahrooz Faghihroohi
- Roger Soberanis
- Tariq Bdair
- Yousef Yeganeh
Teaching
- MLMI
- DLMA
- GDLMA
Extra- curricular:
- GCN learning group
- Scene graph group
Location
Chair for Computer-Aided Medical Procedures and Augmented Reality,
Boltzmannstr. 3, 85748 Garching b. Munchen