README.md

metadata

title: Mammogram Analyzer
emoji: 🏥
colorFrom: pink
colorTo: red
sdk: docker
app_port: 7860

Mammogram Inference Service

A FastAPI microservice that runs mammographic image analysis using the SensiNet dual-stream deep learning model. It accepts a public image URL or a direct file upload, runs Bayesian MC-Dropout inference, and returns a BI-RADS classification with confidence and malignancy probability scores.

This service is the AI backend for the Blossom clinical radiology platform.

---

Authorship and Attribution

This service (the wrapper)

The FastAPI service, inference pipeline, training script, and Blossom integration code in this repository were written by the Blossom team. They are original works that integrate the SensiNet model.

The AI model (SensiNet)

The neural network architecture (app/architecture.py) and pretrained weights (weights/advanced_model_best.pth) are the work of Aredeksu and the SensiNet-Mammography project.

Original model: Aredeksu/SensiNet-Mammography on Hugging Face License: Apache License 2.0 Trained on: CBIS-DDSM mammography dataset

We are integrators and licensees of this model — not its authors or joint authors. Full credit for the model architecture, training methodology, and pretrained weights belongs to the original authors.

In compliance with the Apache 2.0 license:

• The original architecture is reproduced with attribution in app/architecture.py
• Modifications made by us: added the FastAPI wrapper, Bayesian MC-Dropout inference loop, BI-RADS mapping logic, and the training script for fine-tuning
• A copy of the Apache 2.0 license is included in LICENSE

Training data

The model was trained on the CBIS-DDSM (Curated Breast Imaging Subset of DDSM) dataset, a publicly available mammography benchmark dataset from The Cancer Imaging Archive (TCIA).

Lee RS, Gimenez F, Hoogi A, Miyake KK, Gorovoy M, Rubin DL. (2017). A curated mammography data set for use in computer-aided detection and diagnosis research. Scientific Data, 4, 170177. https://doi.org/10.1038/sdata.2017.177

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Architecture (SensiNet)

Input Image (299×299 RGB)
        |
   ┌────┴────┐
   ▼         ▼
Xception   EfficientNet-B3
(2048ch)   (1536ch)
   │         │
   ▼         ▼
 Proj→512  Proj→512
   └────┬────┘
        ▼
  Concat (1024ch)
        ▼
  CBAM Attention
  (channel + spatial)
        ▼
  GlobalAvgPool → Linear(1024→512) → BN → ReLU → Dropout(0.5) → Linear(512→1)
        ▼
  Sigmoid → malignancy probability → BI-RADS 1–5

Inference uses Bayesian MC-Dropout (10 stochastic forward passes) to estimate both the mean malignancy probability and prediction variance, which informs the confidence score.

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Endpoints

Method	Path	Description
GET	/health	Service health check, model mode, version
POST	/predict	Multipart image upload → inference
POST	/analyze	JSON { image_url } → download → inference

/analyze request

{ "image_url": "https://your-storage.supabase.co/storage/v1/object/public/mammograms/..." }

Response shape

{
  "birads": 3,
  "confidence": 0.82,
  "malignancy_probability": 0.31,
  "findings_text": "Model prediction: Benign (probability 31.0%). Probably benign appearance. Short-interval follow-up may be considered.",
  "model_version": "sensinet-v1"
}

BI-RADS mapping:

Probability	BI-RADS	Interpretation
< 10%	1	Negative
10–24%	2	Benign
25–49%	3	Probably benign
50–74%	4	Suspicious
≥ 75%	5	Highly suggestive of malignancy

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Setup

cd mammogram-inference-service
python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

Model weights

Download the pretrained weights from Aredeksu/SensiNet-Mammography on Hugging Face and place the file at:

weights/advanced_model_best.pth

If the weights file is absent, the service automatically falls back to mock mode — a deterministic pixel-statistics-based predictor that returns consistent (but not clinically meaningful) results. Useful for UI development without a GPU.

Environment variables

Variable	Default	Description
MODEL_MODE	real	Set to mock to force mock mode
MODEL_VERSION	sensinet-v1	Version string returned in responses
MODEL_WEIGHTS	weights/advanced_model_best.pth	Path to weights file
ALLOWED_IMAGE_HOSTS	(empty = allow all HTTPS)	Comma-separated allowlist of image hostnames (SSRF protection)

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Run

uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

• Swagger UI: http://localhost:8000/docs
• Health check: http://localhost:8000/health

In your Blossom .env.local:

GCLOUD_MODEL_ENDPOINT=http://localhost:8000

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Training your own weights

If you have a local copy of the CBIS-DDSM dataset, you can fine-tune the model:

# Step 1: organise images into train/val folders
python prepare_data.py \
  --images /path/to/raw/images \
  --csv /path/to/labels.csv \
  --output data

# Step 2: train (two-phase: frozen backbones → full fine-tune)
python train.py --data data --output weights/advanced_model_best.pth

The training script uses the same AdvancedBreastCancerModel architecture as the original SensiNet. Phase 1 trains only the projection layers and classifier head with frozen backbones (20 epochs). Phase 2 fine-tunes all layers at a lower learning rate (50 epochs). Best checkpoint is saved automatically.

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Important notice

This service is intended for research and development use only. It has not been validated for clinical decision-making. Outputs must not be used to diagnose, treat, or manage patients without appropriate clinical oversight and regulatory approval. The BI-RADS scores produced are AI-generated estimates and do not replace radiologist interpretation.

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License

The wrapper code in this repository (FastAPI service, training scripts, integration layer) is original work by the Blossom team.

The SensiNet model architecture and weights are used under the Apache License 2.0. Copyright belongs to the original authors at Aredeksu/SensiNet-Mammography.