Second Look

Second Look is a privacy-preserving, on-device mammogram analysis prototype. This branch (integration/data-pipeline) integrates the Phase 1 pipeline: a binary label mapper (CBIS-DDSM, RSNA, VinDr, INbreast to WORTH_SECOND_LOOK / NOT_WORTH_SECOND_LOOK) with UX tier helpers, a preprocessor (CLAHE, breast masking, pectoral removal, orientation, 224x224 float32 output), an input quality_check gate, a stratified splitter (CBIS's official train/test boundary, or a patient-grouped 70/15/15 for RSNA), GCS retrievers for CBIS-DDSM, RSNA, and VinDr-Mammo, and a MobileNetV2 baseline classifier with a sigmoid head, binary-crossentropy training, and sensitivity-floor evaluation. Trainable datasets: CBIS-DDSM, RSNA Screening Mammography, and VinDr-Mammo are all wired end-to-end through scripts/build_dataset.py and produce a unified data/manifest.csv consumed by modeling/train.py. Tests cover label round-trips, a fixture-gated preprocessing smoke test, mocked-GCS retriever tests, and an end-to-end smoke test on a sampled INbreast subset. See second_look_work_breakdown_structure.docx.

Setup

Requires Python 3.9–3.13. TensorFlow's deeply-nested wheels need Windows long paths enabled (one-time, elevated PowerShell):

New-ItemProperty -Path "HKLM:\SYSTEM\CurrentControlSet\Control\FileSystem" -Name "LongPathsEnabled" -Value 1 -PropertyType DWORD -Force

Then:

py -3.13 -m venv .venv
.venv/Scripts/activate
pip install -r requirements.txt

Testing a dataset

Smoke tests are gated on an env var pointing at the dataset root. This keeps CI green without the data and lets contributors run the full pipeline locally.

INbreast — expected layout: <root>/birads{1..5}/*.png. Samples 5 images per class (25 total), runs quality_check → preprocess → shape assert → augmentation → quality_gate → label round-trip.

Change according to where you have your data stored, but example is:

INBREAST_ROOT="../../data/INbreast dataset - BI-RADS classification/İnbreast" \
  pytest tests/test_smoke_inbreast.py -v

Runtime ~4 min (augmentation runs on full-res images). Expect 2 passed.

Generic fixture — drop a single mammogram at tests/fixtures/sample_mammogram.png and run:

pytest tests/test_smoke.py -v

All tests — pytest tests/ -v. Anything without its env var or fixture skips cleanly.

Getting the CBIS-DDSM dataset

The data lives in gs://b2-foundation/second-look/DDSM/. The bucket holds the four CBIS case description CSVs at the top level and the converted PNG images under cbis_ddsm/. You need read access to the bucket.

One-time setup

Install and initialize the gcloud SDK, then authenticate for Python:

gcloud auth application-default login

This is separate from gcloud auth login. The first one writes the credentials file that the Python google-cloud-storage library reads from. If you only run gcloud auth login, the build script will fail with a DefaultCredentialsError.

To silence the quota project warning (optional):

gcloud auth application-default set-quota-project <your-gcp-project-id>

Building the dataset

The build script downloads the four case CSVs, joins them with the image folder structure in GCS, fetches the PNGs to a local cache, applies the official CBIS train/test split (val is carved out of train at 15%), and writes a training manifest.

Full build (about 10 GB on disk, takes 20 to 40 minutes depending on bandwidth):

python scripts/build_dataset.py --use-gcs --datasets cbis --cache-dir data/cache

Outputs:

• data/manifest.csv is the training manifest, one row per case folder, with split, canonical_label, and image_local_path columns.
• data/manifest_patients.csv is a patient-level audit view.
• data/image_download_report.csv lists the outcome of every download.

The cache is keyed by case folder, so re-running the script is cheap. Already-downloaded PNGs are skipped.

Small local test before the full pull

If you want to confirm everything works before committing to the full download, run with a row cap:

python scripts/build_dataset.py --use-gcs --datasets cbis --cache-dir data/cache --limit 100

That pulls about 100 cases (a few hundred MB) sampled across the train and test splits. Useful for verifying auth, paths, and downstream training code before pulling the full dataset.

Running on Vertex AI instead of your laptop

Training the full model on a laptop is fine for smoke tests but slow for real runs. The bucket lives in GCP, so the natural place to train is a Vertex AI Workbench notebook or a custom training job in the same region. Intra-region reads from GCS are free and fast, and you avoid storing the dataset locally. Point --cache-dir at a local path on the VM (or skip caching entirely and stream from gs:// URIs through tf.io.gfile) and the rest of the pipeline works the same.

This will be implemented soon.

Getting the RSNA dataset

RSNA Screening Mammography Breast Cancer Detection (Kaggle 2022) lives at gs://b2-foundation/second-look/RSNA/rsna-breast-cancer-detection/. Layout:

• train.csv — labels (cancer 0/1) and metadata for 54,706 images across 11,913 patients.
• train_images/{patient_id}/{image_id}.png — pre-converted PNGs.
• test.csv and test_images/ — Kaggle's hidden competition split, no labels. Ignored by the build script.

Because the test labels are unavailable, the build uses a patient-grouped, stratified 70/15/15 split on the labeled train set. All images from one patient (typically 4: L-CC, L-MLO, R-CC, R-MLO) stay in the same partition, so no patient leaks across train/val/test. RSNA labels are per-breast — a patient with unilateral cancer contributes both a positive and a negative image, both landing in the same split. The auth setup is identical to CBIS (ADC).

Full RSNA build:

python scripts/build_dataset.py --use-gcs --datasets rsna --cache-dir data/cache

Small smoke test (~100 images):

python scripts/build_dataset.py --use-gcs --datasets rsna --cache-dir data/cache --limit 100

All datasets at once:

python scripts/build_dataset.py --use-gcs --datasets cbis rsna vindr --cache-dir data/cache

The image manifest gains an image_id column (CBIS rows leave it empty; RSNA and VinDr fill it with the PNG filename stem). The download report is split per-dataset: image_download_report_cbis.csv, image_download_report_rsna.csv, image_download_report_vindr.csv.

Getting the VinDr-Mammo dataset

VinDr-Mammo (PhysioNet 1.0.0) lives at gs://b2-foundation/second-look/VinDR/physionet.org/files/vindr-mammo/1.0.0/. The bucket already holds pre-converted PNGs, so no DICOM dependency is needed. Layout:

• breast-level_annotations.csv — labels (breast_birads strings like "BI-RADS 4") and metadata for 20,000 images across 5,000 studies (one study per patient, four images per study: L-CC, L-MLO, R-CC, R-MLO). Also publishes an official split column with values training and test.
• images/{study_id}/{image_id}.png — pre-converted PNGs.
• finding_annotations.csv — finding-level bounding boxes. Not consumed by the binary pipeline; the build script reads breast-level_annotations.csv only.

Label mapping. breast_birads values span BI-RADS 1 through BI-RADS 5 (no 0, no 6). The retriever parses the string into the integer N before mapping. BI-RADS 4 and 5 map to WORTH_SECOND_LOOK (1); BI-RADS 1–3 map to NOT_WORTH_SECOND_LOOK (0). Configured in config/label_maps.yaml.

Split. VinDr's official train/test boundary is honored via official_split_train_val. Val is carved out of train at 15% grouped by patient_id (= study_id) so a single study's four images cannot straddle the train/val line. Empirically, every VinDr study has uniform BI-RADS across all four images (L/R and CC/MLO match), so the per-breast / per-image distinction does not introduce asymmetric labels the way RSNA does. The auth setup is identical to CBIS (ADC).

Full VinDr build:

python scripts/build_dataset.py --use-gcs --datasets vindr --cache-dir data/cache

Small smoke test (~100 images):

python scripts/build_dataset.py --use-gcs --datasets vindr --cache-dir data/cache --limit 100

Useful flags

• --no-official-split falls back to a stratified 70/15/15 split if you do not want CBIS's canonical split.
• --skip-image-download builds the manifest without pulling images. Handy for inspecting splits and label distributions.
• --max-workers N controls the concurrent download pool (default 8).
• --dry-run prints the resolved source configuration and exits.