Medical Vision AI
Developed ML pipeline for lung function analysis on NVIDIA DGX infrastructure, achieving 10x+ speedup over the existing PREFUL algorithm while improving diagnostic accuracy through CNNs, VAEs, and synthetic data generation.
10x+
Speedup
NVIDIA DGX
Infrastructure
5x
Data Expansion
< 2%
Accuracy Delta
The Challenge
Medical researchers at Leibniz Universität Hannover were using the PREFUL (Phase-Resolved Functional Lung) algorithm for analyzing lung MRI sequences to assess pulmonary function. While accurate, the algorithm was computationally intensive—processing a single patient's scan could take hours on standard hardware, creating a bottleneck in clinical research workflows.
Key Challenges
- •PREFUL algorithm was too slow for practical clinical research timelines
- •Limited availability of labeled medical imaging data for training
- •Need for temporal segmentation across breathing cycles in MRI sequences
- •Strict accuracy requirements for medical diagnostic applications
- •Deployment on high-performance computing infrastructure (NVIDIA DGX)
The Solution
I developed a deep learning pipeline that replaces the computationally expensive components of PREFUL with neural network inference. The system uses CNNs for spatial feature extraction, VAEs for temporal segmentation refinement, and GANs to generate synthetic training data—addressing the limited dataset problem common in medical imaging.
Designed CNN architecture for lung region segmentation from MRI frames
Implemented temporal segmentation using recurrent layers to track breathing phases
Built Variational Autoencoder for result refinement and uncertainty quantification
Developed GAN-based synthetic data generation to augment limited medical datasets
Optimized pipeline for NVIDIA DGX deployment with multi-GPU training
System Architecture
The architecture combines multiple deep learning components in a pipeline that processes MRI sequences end-to-end, from raw frames to functional lung metrics.
MRI Preprocessing Module
Handles DICOM ingestion, intensity normalization, and motion correction. Prepares standardized input tensors for neural network processing.
Spatial Segmentation Network
U-Net based CNN that segments lung regions frame-by-frame. Trained on manually annotated slices with heavy augmentation for robustness.
Temporal Analysis Module
LSTM-based network that processes frame sequences to identify breathing phases (inspiration, expiration, breath-hold). Outputs phase labels for each frame.
VAE Refinement Layer
Variational Autoencoder that refines segmentation boundaries and provides uncertainty estimates. Helps identify cases requiring manual review.
Synthetic Data Generator
StyleGAN-inspired architecture generating realistic lung MRI frames with corresponding labels. Addresses data scarcity while preserving patient privacy.
Key Implementation Details
10x Speedup Through Neural Approximation
The original PREFUL algorithm used iterative optimization that scaled poorly. I identified the computational bottlenecks and replaced them with trained neural networks that approximate the same function. The CNN learns to directly predict what PREFUL computes iteratively, achieving 10x+ speedup while maintaining diagnostic accuracy.
GAN-Based Data Augmentation
Medical imaging datasets are notoriously small due to privacy concerns and annotation costs. I trained a GAN to generate synthetic lung MRI frames conditioned on breathing phase labels. This expanded the effective training set 5x while ensuring generated samples didn't memorize real patient data.
VAE for Uncertainty Quantification
In medical applications, knowing when the model is uncertain is as important as the prediction itself. The VAE component provides calibrated uncertainty estimates—flagging cases where segmentation confidence is low for radiologist review, maintaining clinical safety standards.
DGX-Optimized Training Pipeline
Training on NVIDIA DGX required careful optimization—mixed precision training, gradient checkpointing for memory efficiency, and distributed data parallel across 8 V100 GPUs. Achieved 6x training speedup compared to single-GPU baseline.
Tech Stack
Deep Learning
Medical Imaging
Infrastructure
MLOps
Key Learnings
Neural networks can approximate expensive iterative algorithms with dramatic speedups
Synthetic data generation is crucial for medical imaging where real data is scarce
Uncertainty quantification is non-negotiable in clinical applications
HPC deployment requires different optimization strategies than cloud deployment
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