Polyp Detection with Generative Data Augmentation
Train Faster R-CNN, RetinaNet, and SSD detectors on colonoscopy data, augmented with synthetic images from CycleGAN and SPADE generative models. Hyperparameter search via Optuna & Ray Tune.
Simulated inference
Browser mock · Faster R-CNN
Simulates Faster R-CNN inference on a colonoscopy frame.
The bounding boxes are a browser mock, not model output.
CycleGAN translation
Unpaired image-to-image
CycleGAN learns unpaired mask ↔ polyp translation. SPADE uses spatially-adaptive normalization for mask → polyp synthesis.
Illustrations are schematic. Real CycleGAN outputs are photorealistic colonoscopy images.
Model comparison
10 Faster R-CNN configurations from Optuna HPO
All models are Faster R-CNN (ResNet-50 FPN). Metrics from COCO evaluation on the LDPolypVideo test set.
Detection architectures
| Model | Backbone | Notes |
|---|---|---|
| Faster R-CNN | ResNet-50 FPN | Primary detector, best results |
| RetinaNet | ResNet-50 FPN v2 | Single-stage anchor-based |
| SSD Lite | MobileNet V3 | Lightweight / mobile |
Local Dashboard
The project includes a Streamlit dashboard for interactive model exploration and inference. Clone the repo and run streamlit run src/app.py from code/.