American Cancer Society Machine Learning for Breast Cancer Imaging
The American Cancer Society partnered with Slalom to accelerate and improve the accuracy of breast cancer image analysis for epidemiologic research. Using Google Cloud Cloud ML Engine, Cloud Storage, and TensorFlow, an end-to-end machine learning pipeline was developed for imaging preprocessing, training, and clustering. ML accelerated image analysis by 12 times, improved consistency and objectivity by removing human limitations, and enabled better understanding of breast cancer tissue patterns for future research.
- Organization
- American Cancer Society
- Industry
- Healthcare
- Location
- United States
- Published
- May 2026
Reported outcomes
12x
timeTime & speed
Strategic outcomes
Primary read
Use case focus
Showing 3 of 3
- 1Medical Imaging Analysis
- 2Epidemiologic Research
- 3Machine Learning Pipeline
- Breast cancer image analysis was slow, subjective, and resource-intensive, requiring manual efforts by trained pathologists.
- Processing proprietary raw tissue images into a usable format was technically challenging.
- Implemented an ML pipeline on Google Cloud using Cloud ML Engine for model training and batch prediction.
- Stored and processed images with Cloud Storage, converting them into tiled, color-normalized image tiles for ML feature extraction.
- Used TensorFlow-based autoencoder models and unsupervised deep learning for pattern recognition and clustering of tissue images.
- Achieved 12x faster image analysis for breast cancer research versus manual methods.
- Improved accuracy, consistency, and quality of pathology image interpretations by reducing human fatigue and bias.
- Established a scalable, reliable cloud platform for future epidemiology imaging research.
Architecture
End-to-end ML pipeline uses Cloud Storage for image data, Cloud ML Engine for training and inference, and TensorFlow models for autoencoder-based feature extraction and clustering to identify patterns in pathology images.
Implementation partners1
Sources & evidence1
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