MIC-DKFZ/medicaldetectiontoolkit

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1357 stars · 294 forks observed · 2026-08-28

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

A PyTorch framework providing 2D and 3D implementations of object detectors like Mask R-CNN, Retina Net, and Retina U-Net, tailored for medical image analysis. It includes training and inference pipelines with dynamic patching, prediction consolidation, and object/patient-level evaluation. The project is explicitly no longer maintained in favor of nnDetection.

Use cases

• detect lesions in CT scans with 3D object detection
• train Mask R-CNN on medical images with bounding box annotations
• run instance segmentation on 2D radiology images
• evaluate object detection models on patient-level metrics
• apply Retina U-Net combining segmentation and detection supervision
• patch and tile large 3D medical volumes for training and inference

When to choose

• you need 2D or 3D object detection on medical imaging data with a proven research framework
• you want to reproduce results from the Retina U-Net publication
• you need combined bounding box and pixel-wise annotation training

When to avoid

• you need an actively maintained project - use nnDetection instead
• your use case is general-purpose object detection on natural images
• you require modern PyTorch versions or recent GPU support

Facets

framework · maturity abandoned

machine-learning deep-learning image-processing computer-vision data-science deep-learning computer-vision healthcare machine-learning python object-detection medical-imaging instance-segmentation pytorch 3d-detection mask-rcnn retina-unet medical-image-analysis linux gpu

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markdown · JSON · MCP: product_card(name="MIC-DKFZ/medicaldetectiontoolkit")