lfz/DSB2017

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1241 stars · 412 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

The winning solution of team 'grt123' for the 2017 Data Science Bowl (DSB2017), a deep learning pipeline for detecting lung cancer from CT scans. It uses a 3D Faster R-CNN nodule detector built on a U-net backbone plus a cancer classifier, implemented in PyTorch.

Use cases

• detect lung cancer from CT scans
• train a 3D nodule detector on LUNA16 data
• reproduce the DSB2017 winning solution
• run lung nodule detection on stage 2 test data
• classify lung nodules as cancerous using deep learning
• learn 3D Faster R-CNN for medical imaging

When to choose

• you need a proven lung cancer screening pipeline on CT scans
• you want to reproduce or build on the DSB2017 winning approach
• you have LUNA16 and stage1 data and multiple GPUs for training

When to avoid

• you need a maintained, production-ready medical imaging tool
• you work outside Python 2.7 / CUDA 8 legacy environments
• you lack significant GPU resources, as training takes days on 8 GPUs

Facets

application · maturity maintenance

deep-learning machine-learning image-processing computer-vision healthcare deep-learning machine-learning computer-vision python lung-cancer-detection medical-imaging kaggle-competition 3d-convolutional-networks faster-rcnn pytorch ct-scans luna16 linux gpu

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markdown · JSON · MCP: product_card(name="lfz/DSB2017")