Overview
What this challenge is about.
Triage Brain-CT Stroke Detector with Calibrated Uncertainty. Expert-level challenge in code. Writing production code that solves real engineering problems, e...
The Brief
What you'll do, and what you'll demonstrate.
Train a stroke-detection model with calibrated uncertainty and show how an uncertainty-aware triage order changes time-to-read for top-priority cases.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Apply a 3D / 2.5D CNN to a real medical-imaging classification task
- Estimate model uncertainty via Monte-Carlo dropout
- Calibrate model probabilities and report ECE on held-out hospital data
- Translate uncertainty into operational triage-ordering claims
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Medical Imaging
Apply medical imaging to solve real industry problems and demonstrate production-level capability.
- Convolutional Neural Networks
Apply convolutional neural networks to solve real industry problems and demonstrate production-level capability.
- Uncertainty Quantification
Apply uncertainty quantification to solve real industry problems and demonstrate production-level capability.
- Model Calibration
Apply model calibration to solve real industry problems and demonstrate production-level capability.
- Classification
Apply classification to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
ML Researcher
Calibrated uncertainty + held-out-hospital evaluation are the rigorous portfolio piece radiology-AI labs hire ML researchers on.
This challenge sharpens
- medical-imaging
- uncertainty-quantification
- model-calibration
Computer Vision Engineer
3D / 2.5D CNN training and triage-ordering analysis are core CV-engineer work at any radiology-AI startup.
This challenge sharpens
- convolutional-neural-networks
- classification
- pytorch
Applied AI Scientist
Quantifying the operational benefit of uncertainty-aware triage in time-to-read terms is the applied-AI-scientist's daily work at clinical-AI companies.
This challenge sharpens
- uncertainty-quantification
- model-calibration
- medical-imaging