A 12-week curriculum covering radiology, deep learning, nnU-Net, MONAI, and clinical deployment — everything you need to go from zero to competing in the BraTS Challenge at MICCAI.
Start the 12-Week PathA structured, week-by-week curriculum designed for students. Each week builds on the last — starting from clinical context, through preprocessing and model building, all the way to deployment and advanced radiomics.
Running since 2012, BraTS is the premier benchmark for brain tumor segmentation.
BraTS is an annual challenge at MICCAI, the top conference in medical image analysis. Organized by CBICA (UPenn) with RSNA, ASNR, ESNR, NIH, and FDA. Since 2023, it expanded into a “Cluster of Challenges” with 12+ tasks.
Task 1 — Adult Glioma: Pre- and post-treatment glioma segmentation.
Tasks 2–3 — Meningioma: Pre-treatment and pre-RT meningioma segmentation.
Task 4 — Brain Metastases: 1,475 cases, 4-label system, pre- and post-treatment.
Task 5 — BraTS-Africa: Addressing bias from underrepresentation of sub-Saharan populations.
Task 6 — Pediatric. Task 7 — GOAT (generalizability across tumor types).
Tasks 8–10: Synthesis, inpainting, and histopathology.
Apply your skills elsewhere and build your portfolio.
| Competition | Target | Modality | Details | Link |
|---|---|---|---|---|
| Medical Segmentation Decathlon | 10 organs/tumors | CT & MRI | Generalist benchmark. Rolling leaderboard still active. | medicaldecathlon.com |
| AMOS | 15 abdominal organs | CT & MRI | 500+ cases. Won by nnU-Net in 2022. | grand-challenge.org |
| KiTS | Kidneys & tumors | CT | 300+ cases. Simpler — good stepping stone. | kits-challenge.org |
| HECKTOR | Head & neck tumors | PET/CT & MRI | Multi-modal for radiation therapy planning. | grand-challenge.org |
| ISLES | Stroke lesions | MRI | Brain MRI like BraTS but for stroke. | isles-challenge.org |
| Grand-Challenge.org | Dozens active | Various | Largest biomedical challenge platform. | grand-challenge.org |
| Kaggle Medical Imaging | Various | Various | Beginner-friendly. Often has prize money. | kaggle.com |
Tools you’ll use throughout the curriculum and resources for going deeper.
nib.load('scan.nii.gz').get_fdata() gives you a NumPy array.Hi, I’m Wes Krikorian (Horace Mann Class of 2027). I started MedVision Academy after competing in the 2025 BraTS Challenge at MICCAI.
When I first dove into this, I was starting from scratch with no real background in medicine, radiology, or AI. I spent that summer pretty much living in academic papers, YouTube videos, and GitHub repos, trying to piece everything together through a lot of trial and error. Eventually, I managed to build a MONAI pipeline using three different architectures that ended up taking 2nd place in the competition.
The hardest part of the whole experience wasn’t just the coding — it was how scattered the information was. There wasn’t one place that walked you through the journey from “what is an MRI?” to “here is how you submit predictions.”
I built this site to be that resource. It’s a collection of everything I learned along the way, designed to demystify the BraTS competition and make medical AI research a bit more accessible to any student who’s motivated to dive in.
I hope this website will also be useful for other medical AI competitions and hackathons. Please reach out with comments and content suggestions!
This project would not have been possible without the guidance and support of Dr. Mariam Aboian, Crystal Chukwurah, Dr. Nikolay Yordanov, and Raisa Amiruddin — whose mentorship, educational resources, and work with the BraTS Challenge made both the competition experience and this website possible.
Have a question about the curriculum, the BraTS challenge, or getting started with medical AI research? Found a broken link or want to suggest a resource? Want to share your own experience competing?
Fill out the form and I’ll get back to you as soon as I can.