MammoMix is an open-source deep learning framework for breast cancer object detection in mammography images. It provides training pipelines for YOLOS and Deformable DETR models across three benchmark datasets — CSAW, DMID, and DDSM — along with an ensemble inference system (MoCaE) that combines multiple expert models using calibrated confidence scores, Soft-NMS, and Score Voting.Documentation Index
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Quickstart
Train your first breast cancer detection model in minutes with a step-by-step guide.
Installation
Install MammoMix and its dependencies from the repository.
Architecture Overview
Understand the model architectures, data flow, and design decisions behind MammoMix.
API Reference
Explore the full Python API for datasets, evaluation, and ensemble inference.
What MammoMix offers
YOLOS Training
Train YOLOS-based detectors on CSAW, DMID, or DDSM with a single command.
Deformable DETR
Train Deformable DETR for improved detection of small lesions in mammograms.
MoCaE Ensemble
Combine multiple expert models with calibrated confidence for improved accuracy.
Evaluation Metrics
Evaluate models with mAP, mAP@50, mAP@75, and other detection metrics.
Data Pipeline
Prepare, split, and merge mammography datasets from raw annotations.
Configuration
Control all training hyperparameters through YAML config files.
Supported datasets
MammoMix supports three mammography benchmark datasets out of the box:| Dataset | Description |
|---|---|
| CSAW | Karolinska Mammography dataset for breast cancer screening |
| DMID | Digital Mammography Image Database |
| DDSM | Digital Database for Screening Mammography |
MammoMix uses Pascal VOC XML annotation format. See Annotation Format for details on how annotations are structured and processed.
Getting started
Install dependencies
Clone the repository and install requirements with
pip install -r requirements.txt.Configure your experiment
Copy and edit a YAML config from
configs/ to set your model, dataset, and hyperparameters.