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license: other
license_name: physionet-credentialed-health-data-license-150
license_link: https://physionet.org/content/mimic-iv-echo/view-license/0.1/
extra_gated_heading: "Access to MIMIC-IV-Echo V-JEPA2 Embeddings"
extra_gated_description: >
This dataset contains metadata derived from MIMIC-IV-Echo, which is a credentialed dataset on PhysioNet.
To access this dataset, you must have an active PhysioNet credentialed account with signed
Data Use Agreement (DUA) for MIMIC-IV-Echo.
extra_gated_fields:
PhysioNet username: text
I have an active credentialed account on PhysioNet: checkbox
I have signed the MIMIC-IV-Echo Data Use Agreement: checkbox
I agree to not redistribute this data: checkbox
Affiliation: text
tags:
- medical-imaging
- echocardiography
- embeddings
- mimic-iv-echo
- v-jepa2
- video-embeddings
- self-supervised-learning
---
# MIMIC-IV-Echo V-JEPA2 Embeddings
Pre-computed video embeddings for [MIMIC-IV-Echo](https://physionet.org/content/mimic-iv-echo/) echocardiography videos, extracted using [V-JEPA2](https://github.com/facebookresearch/jepa) (Meta's self-supervised video encoder).
> **Access requirement:** This dataset includes metadata from MIMIC-IV-Echo (subject IDs, study IDs, timestamps, clinical note references). You must have [PhysioNet credentialed access](https://physionet.org/settings/credentialing/) and a signed DUA for [MIMIC-IV-Echo](https://physionet.org/content/mimic-iv-echo/) before requesting access.
## Dataset
| | |
|---|---|
| **Videos** | 525,328 echocardiography clips |
| **Subjects** | ~4,800 patients |
| **Studies** | ~7,200 echo studies |
| **Embedding model** | V-JEPA2 ViT-L (300M params) |
| **Embedding dim** | 1024 (float32) |
| **Format** | Sharded Parquet (10 shards, ~2.1 GB total) |
## Structure
```
mimic-iv-echo-jepa-embeddings/
└── jepa-l-embeddings/
├── train-00000-of-00010.parquet (p10, 51K rows, 202 MB)
├── train-00001-of-00010.parquet (p11, 52K rows, 205 MB)
├── ...
└── train-00009-of-00010.parquet (p19, 53K rows, 208 MB)
```
Each shard corresponds to one MIMIC-IV patient folder (p10-p19).
## Columns
| Column | Type | Source | Description |
|--------|------|--------|-------------|
| `subject_id` | int64 | echo-record-list.csv | MIMIC patient ID |
| `study_id` | int64 | echo-record-list.csv | Echo study ID |
| `dicom_id` | str | filename | Original DICOM identifier (e.g. `94106955_0001`) |
| `file_path` | str | embedding key | Relative path to source MP4 |
| `acquisition_datetime` | str | echo-record-list.csv | Per-video acquisition timestamp |
| `study_datetime` | str | echo-study-list.csv | Per-study timestamp |
| `note_id` | str | echo-study-list.csv | Clinical note reference (nullable) |
| `note_seq` | str | echo-study-list.csv | Note sequence number (nullable) |
| `note_charttime` | str | echo-study-list.csv | Note chart time (nullable) |
| `embedding` | list[float32] | V-JEPA2 ViT-L | 1024-dim video embedding |
Metadata is joined from two MIMIC-IV-Echo CSV files so each row is self-contained:
- **echo-record-list.csv** (525K rows) — per-video: subject, study, acquisition time
- **echo-study-list.csv** (7K rows) — per-study: study time, clinical notes
## Usage
```python
from datasets import load_dataset
ds = load_dataset("MITCriticalData/mimic-iv-echo-jepa-embeddings")
print(ds["train"][0]) # {'subject_id': 10002221, 'embedding': [...], ...}
```
```python
import pyarrow.parquet as pq
table = pq.read_table("jepa-l-embeddings/")
print(table.num_rows) # 525328
print(table.schema)
```
## Extraction
Embeddings were extracted using the pipeline described in [readme-embeddings.md](https://github.com/sebasmos/EchoJEPA-VE/blob/main/readme-embeddings.md).
| Step | Command |
|------|---------|
| Extract (SLURM) | `sbatch extract_slurm.sh vitl` |
| Merge .pt files | `python merge_embeddings.py --model vitl` |
| Convert to Parquet | `python to_parquet.py --model vitl` |
Config: L40S GPU, batch=256, 8 DataLoader workers, ~30 min per folder.
## Citation
If you use this dataset, please cite both the original MIMIC-IV-Echo dataset and V-JEPA2:
```bibtex
@article{mimic-iv-echo,
title={MIMIC-IV-Echo: A Large-Scale Echocardiography Dataset},
note={PhysioNet, https://physionet.org/content/mimic-iv-echo/}
}
@article{bardes2025vjepa2,
title={Revisiting Feature Prediction for Learning Visual Representations from Video},
author={Bardes, Adrien and Garrido, Quentin and Ponce, Jean and Chen, Xinlei and Rabbat, Michael and LeCun, Yann and Assran, Mahmoud and Ballas, Nicolas},
year={2025}
}
```
|