Pixels To Prognosis

About the Event

Gliomas are among the most aggressive brain tumours, and outlining them accurately on MRI is central to diagnosis, surgical planning and treatment monitoring. Doing it by hand is slow, and different radiologists often draw the boundaries differently.

Pixels to Prognosis asks you to build an AI system that does this automatically. Using multimodal brain MRI scans, your model has to locate a glioma and separate it into its three subregions: active (enhancing) tumour, necrotic core, and surrounding edema.

The event combines neuroscience, MRI physics and computer vision. You’ll need more than a model with a good leaderboard score. You’ll also need to understand what the scans show, why models fail, and how to make one that holds up on real clinical data.

Who Can Participate

• Open to undergraduate and postgraduate students from any college and any stream

• Especially suited to students in Biotechnology & Biomedical Engineering, Computer Science, and AI & Data Science

• Teams of 1 to 3 members. Cross-disciplinary teams are encouraged.

Competition Structure

Round 1: Online Quiz (Screening)

This is a case-based quiz on:

• How MRI machines work and the basics of MRI imaging

• The biology of brain gliomas and how they appear on MRI scans

• Artificial Intelligence, Deep Learning and Image Processing

The questions reward reasoning over memorisation. The quiz is meant to sharpen your problem-solving for the later rounds.

Round 2: Model Submission Round

Shortlisted teams train a segmentation model on a curated glioma dataset we provide, [derived from the BraTS 2021 challenge]. Each patient has four MRI modalities: T1-weighted, post-contrast T1-weighted (T1ce), T2-weighted and T2-FLAIR.

Your model must segment three regions: Whole Tumour, Tumour Core and Enhancing Tumour. Models are ranked on a hidden patient-level test set.

Rules:

• Train only on the dataset provided. No external data.

• All learnable parameters must start from random initialisation. Pretrained weights are not allowed. You may use published architectures such as U-Net variants.

• The Colab notebook runtime must be 9 hours or less.

Submission requirements:

• Complete source code (.py / .ipynb)

• Final trained weights (.pt / .keras)

• Training logs, including loss and validation curves, config and seed used (.csv)

• A technical report (.pdf) covering your thought process, pipeline, architecture details and the compute you used

Evaluation: Segmentation quality…

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Curated listing from Unstop. Organized by Indian Institute of Technology (IIT), Madras. RSVP happens on the organizer's page until they claim it. Run this event? Claim it on Jamao to get free RSVPs, WhatsApp reminders and UPI payouts.