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
M.V Labs' research leads set the target: cut inference time and the false-positive rate without sacrificing accuracy. GTEMAS augmented their R&D team with 2 Computer Vision Engineers and 1 MLOps specialist to execute it. Working within the existing research group, our engineers rewrote the inference pipeline from Python to optimized C++ using TensorRT, and redesigned the model as a 3D U-Net that considered volumetric context instead of scanning slice by slice, which cut the false positives that had been stalling the FDA submission.
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)
False-positive rate cut enough to clear the FDA review bottleneck
Passed FDA 510(k)
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