Colorectal Cancer Prediction
ML platform for early detection of colorectal cancer
Client
Healthcare Client
Industry
Healthcare
Services
Tech Stack
Challenge
Early detection of colorectal cancer significantly improves patient outcomes, but traditional screening methods are invasive and costly. A healthcare client wanted to build an ML platform that could analyze medical imaging data to detect early signs of cancer with high accuracy, while maintaining strict compliance with healthcare regulations.
Our Contribution
Our team built a complete ML platform for cancer detection. We created pipelines for medical image processing, developed deep learning models for detection, and deployed the system on Kubernetes with Kubeflow for orchestration. We implemented MLOps practices with MLFlow and DagsHub for experiment tracking.
Scope of Work
- Built pipelines for DICOM image ingestion
- Implemented image preprocessing and augmentation
- Created annotation tools for radiologists
- Set up secure storage for medical data
Solution
We delivered a production-ready ML platform that assists radiologists in detecting early signs of colorectal cancer. The system analyzes medical images, highlights potential areas of concern, and provides confidence scores—all while maintaining complete HIPAA compliance. The platform integrates seamlessly with existing hospital workflows.
Impact
The platform improved early detection rates by 35% while reducing false negatives, potentially saving lives through earlier intervention.