Glioma Detection
Services
The big question
How can we use computer vision to highlight glioma-relevant regions while keeping the pipeline interpretable?
The Objective
a medical imaging ML pipeline for detection and prioritization, designed to support review rather than replace.
This project explores machine learning for medical imaging, specifically focusing on glioma detection in MRI scans. We developed a computer vision pipeline that highlights suspicious regions, assisting clinical review workflows without replacing human expertise. The system focuses on high recall and interpretability, providing radiologists with a reliable second opinion and streamlining the diagnostic process.
Visual direction
The visual identity for Glioma Detection AI is designed to convey "Precision and Empathy." We utilized a clinical but approachable palette of soft hospital blues, crisp whites, and gentle amber highlights for anomaly detection. Central to the design are high-fidelity 3D volumetric renderings of MRI data, which allow clinicians to rotate and explore neural structures with surgical accuracy. The interface is purposefully clean, using generous white space to focus attention on critical diagnostic findings. Every line of the UI, from the fine-grained segmentation masks to the intuitive statistical overlays, was iterated to ensure absolute clarity in high-stakes medical environments, blending cutting-edge AI with a human-centric clinical aesthetic.
Focus on Experience
The diagnostic experience is built on trust and efficiency. Radiologists are guided through an automated pre-screening workflow where AI-detected candidates are prioritized for immediate review. Interactive MRI slice viewers provide frame-by-frame analysis with intelligent segmentation overlays that can be toggled on and off to verify AI predictions. We focused on reducing "alert fatigue" by only surfacing high-confidence anomalies, integrating directly into PACS systems for a frictionless workflow. Glioma Detection AI transforms dense medical imaging data into a clear, actionable diagnostic path, empowering medical professionals to make faster, more accurate life-saving decisions.
Glioma Detection AI has been integrated into several pilot clinics, showing a significant improvement in early tumor detection rates during initial trials.
High-precision medical image classification.
