MedTech R&D: Accelerating Cancer Detection with Computer Vision
Augmenting a research team to bring a life-saving device to FDA trials.
The Challenge
M.V Labs had a brilliant algorithm but slow execution. Their model took 5 minutes to process a scan, making it unusable in clinical workflows. They lacked engineers who understood both Deep Learning and High-Performance Computing (C++/CUDA).
The Solution
GTEMAS provided 2 Computer Vision Engineers and 1 MLOps specialist. We rewrote the inference pipeline from Python to optimized C++ using TensorRT. We implemented a 3D U-Net architecture that considered volumetric context, significantly reducing false positives.
Architectural Strategy
Hybrid cloud architecture processing DICOM images on edge servers using NVIDIA Triton Inference Server.
Impact & Achievements
The speed optimization was the key to commercial viability. The product is now deployed in 40+ hospitals across the EU.
Processing time (down from 5m)
Sensitivity achieved
Passed FDA 510(k)
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