✅ Develop a Three-Stage AI Pipeline for Spine MRI

Use when: Implementing AI solutions for diagnostic imaging in healthcare

How it works:

• Segmentation Stage (U-Net)

Identify 17+ anatomical structures (e.g., dural sac, vertebrae) using U-Net, establishing geometric and semantic baselines.

• Multi-label Classification

Detect stenosis in key regions — central canal, lateral recess, foraminal openings.

• Severity Assessment (RegNetY32GF)

Grade severity using composite input (segmentation masks + axial slices) to improve contextual accuracy.

Tip: Use multi-vendor DICOM datasets to ensure robustness across different scanners.

Tool: U-Net, RegNetY32GF

Outcome: Context-aware AI pipeline aligned with radiologist workflow.

✅ Build Explainability into AI Models

Use when: Increasing trust and transparency in AI diagnostic tools

How it works:

• Pair predictions with Grad-CAM visualizations to highlight AI attention.
• Overlay anatomical masks and feature anchors directly on DICOM images.
• Visualize the why, not just the what.