Enhancing Gastrointestinal Diagnostics with Polyp Detection in Colonoscopy Images on Matrice
Polyp detection in colonoscopy images is a critical task in gastrointestinal diagnostics, enabling early identification of precancerous lesions to improve patient outcomes. AI-powered computer vision can accurately detect and localize polyps in real-time, assisting endoscopists and enhancing diagnostic precision.
This blog details how Matrice enables no-code AI deployment for polyp detection using deep learning, covering:
Dataset Preparation
Dataset Annotation
Model Training
Model Evaluation
Model Inference
Model Deployment
Dataset Preparation
The dataset for this project comprises 1,000 annotated colonoscopy images, labeled to identify polyp and non-polyp regions. The dataset is carefully partitioned to support robust model training, validation, and evaluation:
Total Samples: 1,000
Training Set: 700
Validation Set: 200
Testing Set: 100
Model Training
We utilized a YOLO-based object detection model to detect and classify polyps in colonoscopy images. YOLOv8 was chosen for its high accuracy and efficiency, ideal for analyzing medical imaging with subtle visual differences.
Model: YOLOv8 (multi-class object detection)
Batch Size: 16
Epochs: 90
Learning Rate: 0.001
Optimizer: Auto
Momentum: 0.95
Weight Decay: 0.0005
Model Evaluation
The trained model was evaluated on the test dataset, using key metrics to assess its effectiveness in detecting polyps.
Metric |
Value (Test) |
|---|---|
mAP@50 |
0.91 |
mAP@50-95 |
0.75 |
Recall |
0.90 |
Precision |
0.88 |
Model Inference
The trained model supports export to multiple formats, enabling deployment across various platforms, from endoscopic systems to cloud-based diagnostic tools.
Supported formats include:
PyTorch (.pt)
ONNX
TensorRT
OpenVINO
This flexibility ensures compatibility with diverse deployment scenarios, such as clinical endoscopy suites, telemedicine platforms, or research environments.
Model Deployment
Using Matrice, the trained model can be deployed seamlessly via a no-code interface. Matrice supports:
Real-time inference
API-based integration
Visual dashboards for monitoring
Applications include:
Automated polyp detection during colonoscopy procedures
Decision support for endoscopists
Integration into telemedicine and diagnostic platforms
Conclusion
AI-powered polyp detection in colonoscopy images enhances diagnostic accuracy and supports early intervention for gastrointestinal health. With Matrice, deploying such solutions is streamlined, enabling rapid integration into clinical workflows with reduced costs. By leveraging YOLOv8 and a well-curated dataset, you can develop high-performing models to transform gastrointestinal diagnostics with visual intelligence.
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