Medical Image Diagnostic Assistant
Impact94% sensitivity achieved
Sensitivity94%
Specificity92%
Scans/Day500+
Overview
Built a medical image analysis system that assists radiologists in detecting abnormalities in X-rays and CT scans. The system uses CNN architectures optimized for medical imaging to highlight regions of interest.
The Challenge
Radiologists face high workloads and time pressure, leading to potential missed diagnoses. They needed an AI assistant to provide second opinions and prioritize urgent cases.
The Solution
Developed custom CNN models trained on medical imaging datasets, with attention mechanisms to highlight suspicious regions. Implemented DICOM integration and confidence scoring for clinical workflows.
Key Results
94% sensitivity in anomaly detection
Reduced review time by 40%
Processing 500+ scans daily
Tech Stack
PythonPyTorchMONAIOpenCVDICOMFastAPIDocker