## Grand X-Ray Slam: Division A
A Kaggle Community Hackathon - Build AI for Life-Saving Radiology

### Overview
Welcome to Grand X-Ray Slam: Division A, the first of a 2-part Kaggle hackathon series where data scientists and AI enthusiasts compete to advance medical imaging. In this challenge, you’ll develop AI models to detect 14 thoracic conditions from chest X-rays, tackling real-world clinical complexity. Your work will power Dr HealthAgent a personal Health app that I had developed under my company Blue and Gold Healthcare Inc., enhancing global healthcare.

Top performers across both Division A and Division B will also shine on the Grand Slam Leaderboard, sharing an additional $2,500 prize pool.

### Your Mission
Build AI models to identify the following conditions in each X-ray:

- Atelectasis
- Cardiomegaly
- Consolidation
- Edema
- Enlarged Cardiomediastinum
- Fracture
- Lung Lesion
- Lung Opacity
- No Finding
- Pleural Effusion
- Pleural Other
- Pneumonia
- Pneumothorax
- Support Devices

This is **multi-label classification**, reflecting real emergency-room diagnostics.

### Dataset
Train set: 107,374 images (~138 GB)
Test set: 46,233 images (~60 GB) 
- Images are de-identified chest X-rays from multiple institutions.

### Timeline
- Start: August 20, 2025  
- End: October 10, 2025, 11:59 PM UTC  
- Private Leaderboard Release: October 12, 2025, 23:59 UTC

### Participation Rules
- Teams: Solo or up to 4 members  
- External data: Not allowed  
- Use: Competition & research only  

### Acknowledgements
**Organizers:**  
- Guntas Dhanjal (Lead)  
- Salah Sammari  
- Fathi Ben Amor  
- Mariem Aissa  

**Data Sources:** Multiple publicly available chest X-ray datasets, fully anonymized and preprocessed to ensure no patient overlap between train and test sets