Image-Quality Triage Tool for a Tele-Radiology Network
Overview
What this challenge is about.
You receive 10,000 chest-X-ray images with multi-label quality flags (rotation, clipping, motion). Train a small multi-label CNN that outputs a per-flag probability and a single 'diagnostic / non-diagnostic' decision via a learned threshold. Evaluate per-flag F1 + the binary decision's sensitivity at 95% specificity. Build a small Gradio demo. Produce a 3-page product memo discussing where in the technologist workflow this should land and the false-positive budget the team should accept.
The Brief
What you'll do, and what you'll demonstrate.
Build and demo an image-quality triage model for chest X-rays that flags non-diagnostic images at acquisition with a defended false-positive budget.
Earning criteria — what you'll demonstrate
- Apply multi-label CNN classification to medical-imaging quality control
- Translate per-flag probabilities into a single workflow decision
- Defend a false-positive budget against operational impact
- Demo a working ML tool in a workflow-relevant interface
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Computer Vision Engineer
Shipping a CNN-based quality-triage tool with a working demo is exactly the day-one CV-engineer deliverable at tele-radiology and imaging-vendor startups.
This challenge sharpens
- medical-imaging
- convolutional-neural-networks
- classification
AI Engineer
Wrapping a multi-label model into a workflow-placed demo with a defended false-positive budget is the AI-engineer's bread-and-butter at applied healthtech teams.
This challenge sharpens
- demo-development
- classification
- pytorch
Applied AI Scientist
Tying model decisions to operational metrics like technologist retake load is the applied-AI-scientist's craft at any healthtech product team.
This challenge sharpens
- model-evaluation
- medical-imaging
- classification