Prove the Pipeline First: A Knee MRI Baseline on 100GB You Never Download
The RSNA Knee Abnormality Detection competition is a different animal from the tabular problems I usually work. The task sounds simple: predict 12 binary findings per knee MRI study (ACL tear, meniscus, effusion, fracture, and so on), scored by macro-averaged ROC AUC. The reality is harder:
- 4,407 studies, 24,371 series, roughly 820,000 DICOM slices, 100GB+.
- You do not download any of it. The data is mounted inside a Kaggle notebook and you train on Kaggle’s GPU.
- Only 58 of the 4,407 studies are expert-labeled. The rest come with a free-text radiology report, in a dozen languages.
So before writing a model, I wanted to answer one question: can I even reach the data, read it, and get a valid submission accepted? If that plumbing does not work, a fancy model is worthless.