Solutions

I am passionate about and focused on bringing AI-based healthcare solutions that make a difference. My work spans a broad portfolio of AI applications in Medical Imaging that aid in fast and efficient clinical decision-making. 

AI-Rad Companion Chest X-ray

Global Product Manager, Siemens Healthineers, Germany

The application helps to detect radiographic findings on Chest X-ray images with the use of AI. The algorithm can characterize and highlight the findings of pulmonary lesions, pleural effusion, pneumothorax, consolidation, and atelectasis. The output from the AI-Rad Companion Chest X-ray is used in concurrent-read mode to support radiologists in their differential diagnosis and clinical decision-making.

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Computational Neuroimaging

Research Scientist,

German Center for Neurodegenerative Diseases, Bonn, Germany

  • Contribution

    • Fully convolutional architectures for training and quality control with limited annotations through intelligent transfer learning and Bayesian Deep Learning.

    • Learning based image reconstruction from sparsely sampled complex valued k-space MRI data through Complex Fully Convolutional Networks.

    • Evaluated robustness of encoder-decoder architectures to adversarial attacks.

  • Proof-of-concept: Robust and fast whole brain segmentation and cortical parcellation (102 semantic classes) for investigating normal brain development, aging and neuro-degeneration.

  • Impact: Achieved whole brain segmentation in under 20 seconds. FreeSurfer neuro-image analysis pipeline accelerated ten-fold.  

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Evidence Based Image Understanding

Doctoral Candidate,
CAMPAR (Prof. Dr. Nassir Navab)

Technische Universität München 

  • Contribution: Developed fast and scalable learning to hash methods (binary semantic indexing) to explore and exploit large-scale heterogeneous medical databases. 

  • Proof-of-concept: Demonstrated for semantics-preserving retrieval of neuronal images, searching chest X-rays with co-morbidities and multiple instance retrieval in mammography and histopathology.

  • Impact: Effectively overcame prohibitive computational complexity and memory overheads associated with image retrieval.   

 

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Retinal Layer Segmentation

Graduate Student, CAMPAR (Prof. Dr. Nassir Navab)

Technische Universität München 

Fully convolutional deep architecture, termed ReLayNet, for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans.

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Computational Cardiology

Graduate Student, CAMPAR (Prof. Dr. Nassir Navab)

Technische Universität München 

  • Contribution: Investigated automated techniques for tissue-characterization and segmentation (with domain adaptation) through computational modeling of modality physics (tissue-energy interaction).

  • Proof-of-concept: Demonstrated for in vivo tissue characterization in intravascular ultrasound and optical coherence tomography for assessment of atherosclerotic plaques.   

  • Impact: Effective in learning under high anatomical variability, extreme class imbalance and presence of pathology associated with in vivo tissue characterization.

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