Lung Cancer Detection System
A high-precision diagnostic engine utilizing Vision Transformer (ViT) models to automate cancer detection from CT scans.
Validation accuracy on test sets
Processing time per CT slice
End-to-end patient data encryption
The Medical Challenge
Manual analysis of hundreds of CT scan slices is a bottleneck in modern radiology, often leading to fatigue-induced errors. Early detection is critical for patient survival, necessitating high-velocity screening tools that can prioritize urgent cases for radiologists.
The objective was to adapt the latest advancements in computer vision—specifically Vision Transformers—to the nuances of 3D medical imaging, maintaining both high sensitivity and specificity.
Architectural Solutions
Vision Transformer (ViT) Core
Unlike standard CNNs, the ViT architecture uses self-attention mechanisms to capture global dependencies across the image. By fine-tuning a pre-trained ViT on localized medical datasets, we achieved a significant boost in detecting subtle nodules that traditional models often missed.
Dockerized Inference Workers
To manage the heavy computational load of transformer models, I architected a distributed backend using FastAPI and Docker. Model inference is decoupled from the user interface, allowing for horizontal scaling of "detection workers" based on queue volume.
Technical Achievements
- Achieved 93.4% validation accuracy on the LUNA16 dataset.
- Implemented model quantization to reduce memory footprint by 60%.
- Developed a secure DICOM processing pipeline for anonymization.
- Built a real-time heatmap visualization for radiologist review.
Research Stack
AI for Impact?
I specialize in taking cutting-edge AI research and turning it into production-ready software systems.
Discuss Your ProjectStart an engineering partnership.
Providing technical leadership to ensure your project's success through scalable architecture and clean code.
Response SLA
Guaranteed within 24 hours
Confidentiality
100% Secure & NDA Compliant
Contact Form
Reach out and I'll get back to you shortly.