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Datasets:
benbarkow
/
test-us-ssl

Tasks:
Feature Extraction
Size:
n<1K
Tags:
ultrasound
self-supervised
rf-data
mae
jepa
License:
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test-us-ssl / README.md
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metadata 
pretty_name: SSL Ultrasound Representation (Stanford RF MultiFocal)
license: other
task_categories:
  - feature-extraction
tags:
  - ultrasound
  - self-supervised
  - rf-data
  - mae
  - jepa
size_categories:
  - n<1K

SSL Ultrasound Representation Dataset

Beamformed multi-focal ultrasound RF data from the Stanford Ultrasound RF Channel dataset, aggregated for self-supervised pre-training (MAE / JEPA / contrastive).

Files

File Description
ultrasonic_dataset.zarr.zip 6D float32 zarr array, Blosc-LZ4 compressed (2.4 GB).
ultrasonic_dataset.metadata.json Per-frame provenance (subject, file, subfolder, indices).
README.md This card (auto-generated by push_to_hf.py).

Array layout

  • Name: rf_iq
  • Shape: (14, 192, 192, 3, 2, 215)
  • Axes: ['frame', 'transducer', 'scanline', 'focal_zone', 'iq', 'sample']
  • Chunks: (1, 32, 32, 1, 2, 215) (one frame per chunk on the leading axis — aligned with DataLoader workers).
  • Dtype: float32
  • IQ index: 0 = in-phase, 1 = quadrature.

Processing pipeline

  1. Source: Verasonics Vantage .mat files with MultiFocal in the filename (each file holds a 2-frame cine via Resource.RcvBuffer.numFrames = 2).
  2. Sub-aperture combination + IQ demodulation via pymust.rf2iq.
  3. Fast-time axis truncated at 860 samples (everything beyond is noise/zeros), then decimated by 4× → 215 output samples per signal.
  4. Stacked into a single 6D array with chunks optimised for 3D MAE patching.

See scripts/dataset_scripts/build_mae_dataset.py in the source repo for the exact pipeline; see scripts/dataset_scripts/visualize_beamformed_pymust.py for the reference beamformer.

Frame counts

By subject:

Subject Frames
rat1 8
rat2 6

By subject × subfolder:

Subject Subfolder Frames
rat1 — 6
rat1 exposed_liver 2
rat2 — 6

Loading 

from src.dataset import build_split_datasets

splits, meta = build_split_datasets(
    "benbarkow/test-us-ssl",
    split_strategy="file",  # or "subject"
    cache_dir="/tmp/hf_cache",
)
train_ds = splits["train"]

The split helpers (split_by_subject, split_by_file) avoid cross-split leakage: both frames of the same 2-frame cine always end up in the same split, and (for split_by_subject) no subject appears in two splits.