| --- |
| license: other |
| license_name: ophthalmology-mixed |
| license_link: https://github.com/mayberichard/zju-eye-pretrain/blob/main/LICENSE |
| task_categories: |
| - image-classification |
| - image-segmentation |
| - image-to-image |
| - text-to-image |
| - unconditional-image-generation |
| language: |
| - en |
| - zh |
| tags: |
| - ophthalmology |
| - retina |
| - oct |
| - fundus |
| - slo |
| - medical-imaging |
| - segmentation |
| - pretraining |
| size_categories: |
| - 1M<n<10M |
| pretty_name: ZJU Eye-Pretrain (Private Shanghai Topcon + 25 public cohorts) |
| configs: |
| - config_name: private_topcon |
| data_files: |
| - split: train |
| path: data/private_topcon/*.parquet |
| - config_name: public_fundus |
| data_files: |
| - split: train |
| path: data/public_fundus/*.parquet |
| - config_name: public_oct |
| data_files: |
| - split: train |
| path: data/public_oct/*.parquet |
| - config_name: drive_vessel |
| data_files: |
| - split: train |
| path: data/public_fundus/public_drive_vessel-*.parquet |
| - config_name: eyepacs_combo_dr_aug |
| data_files: |
| - split: train |
| path: data/public_fundus/public_eyepacs_combo_dr_aug-*.parquet |
| - config_name: gamma_multimodal |
| data_files: |
| - split: train |
| path: data/public_fundus/public_gamma_multimodal-*.parquet |
| - config_name: idrid |
| data_files: |
| - split: train |
| path: data/public_fundus/public_idrid-*.parquet |
| - config_name: messidor2_dr |
| data_files: |
| - split: train |
| path: data/public_fundus/public_messidor2_dr-*.parquet |
| - config_name: amd_sd |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_amd_sd-*.parquet |
| - config_name: areds2 |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_areds2-*.parquet |
| - config_name: aroi |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_aroi-*.parquet |
| - config_name: c8 |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_c8-*.parquet |
| - config_name: chiu_dme_2015 |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_chiu_dme_2015-*.parquet |
| - config_name: glaucoma |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_glaucoma-*.parquet |
| - config_name: kermany |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_kermany-*.parquet |
| - config_name: neh_ut_2021 |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_neh_ut_2021-*.parquet |
| - config_name: nyu_poag |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_nyu_poag-*.parquet |
| - config_name: octa500 |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_octa500-*.parquet |
| - config_name: octdl |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_octdl-*.parquet |
| - config_name: octid |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_octid-*.parquet |
| - config_name: oimhs |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_oimhs-*.parquet |
| - config_name: olives |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_olives-*.parquet |
| - config_name: retouch |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_retouch-*.parquet |
| - config_name: sparsity_sdoct_2012 |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_sparsity_sdoct_2012-*.parquet |
| - config_name: srinivasan_2014 |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_srinivasan_2014-*.parquet |
| - config_name: thoct1800 |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_thoct1800-*.parquet |
| - config_name: uestc |
| data_files: |
| - split: train |
| path: data/public_oct/public_oct_uestc-*.parquet |
| - config_name: refuge2_disc_cup |
| data_files: |
| - split: train |
| path: data/public_fundus/public_refuge2_disc_cup-*.parquet |
| - config_name: private |
| data_files: |
| - split: train |
| path: data/private_topcon/shanghai_drioct_triton-*.parquet |
| --- |
| |
| # ZJU Eye-Pretrain Dataset |
|
|
| > Unified multi-source ophthalmological imaging dataset for foundation model pretraining and downstream tasks. |
|
|
| **1.1M images** spanning **26 cohorts** with a **strict 41-column unified manifest schema**. |
|
|
| ## Composition |
|
|
| | Source | Images | Modalities | Cohorts | |
| |---|---:|---|---| |
| | Private Shanghai DRI OCT Triton (SS-OCT) | 419,042 | oct_bscan + fundus_color + slo_gray | 1 | |
| | Public Fundus | 198,629 | fundus_color (+ GAMMA OCT) | 6 | |
| | Public OCT | 488,705 | oct_bscan | 19 | |
| | **Total** | **1,106,376** | | **26** | |
| |
| See [DATASET_OVERVIEW.md](DATASET_OVERVIEW.md) for full details per cohort (devices, regions, masks, demographics). |
| |
| ## Quick Start |
| |
| ```python |
| from datasets import load_dataset, concatenate_datasets, Image |
| |
| # === Load by batch === |
| ds_priv = load_dataset("mayberichard/zju-eye-pretrain", "private_topcon") |
| ds_fun = load_dataset("mayberichard/zju-eye-pretrain", "public_fundus") |
| ds_oct = load_dataset("mayberichard/zju-eye-pretrain", "public_oct") |
|
|
| # === Load by single cohort (28 available, see configs in YAML above) === |
| ds = load_dataset("mayberichard/zju-eye-pretrain", "kermany") # 109k |
| ds = load_dataset("mayberichard/zju-eye-pretrain", "octa500") # 120k |
|
|
| # === IMPORTANT: cast binary columns to Image for auto-decode === |
| ds = ds.cast_column("image", Image()) |
| # For cohorts with masks (DRIVE/IDRiD/REFUGE2/AROI/OIMHS/AMD-SD/Chiu/Glaucoma/OCTA500/RETOUCH): |
| for col in ds["train"].features: |
| if col.endswith("_mask") and str(ds["train"].features[col]) == "Value('binary')": |
| ds = ds.cast_column(col, Image()) |
| |
| # Each row after cast: |
| # image: PIL.Image (auto-decoded) |
| # {vessel|fov|layer|lesion|disc_cup|...}_mask: PIL.Image or None |
| # image_id, study_id, patient_hash, modality, anatomy, severity, diagnosis_group, ... |
|
|
| # === Concat 3 batches manually if needed === |
| # Note: schemas differ across batches (mask column sets), so use only shared cols: |
| shared = ["image_id", "cohort", "study_id", "patient_hash", "modality", |
| "anatomy", "device_vendor", "device_model", "severity", |
| "diagnosis_group", "image", "bscan_index"] |
| all_ds = concatenate_datasets([ |
| ds_priv["train"].select_columns(shared).cast_column("image", Image()), |
| ds_fun["train"].select_columns(shared).cast_column("image", Image()), |
| ds_oct["train"].select_columns(shared).cast_column("image", Image()), |
| ]) |
| # 1.1M images total, mask cols dropped (use per-batch load if you need masks) |
| |
| # === Streaming for big training runs (avoids downloading all 287 GB) === |
| ds = load_dataset("mayberichard/zju-eye-pretrain", "public_oct", streaming=True) |
| ds = ds.cast_column("image", Image()) |
| for row in ds["train"]: |
| img = row["image"] # PIL Image, lazy-decoded |
| ... |
| ``` |
| |
| ## Schema (41-column manifest, identical across all batches) |
| |
| ``` |
| cohort, study_id, patient_hash, visit_date, eye, |
| device_vendor, device_model, device_serial_hash, device_software_version, |
| hospital_domain, ethnicity, |
| image_quality_score, image_quality_band, |
| diagnosis_group, lesion_tags, lesion_location, layer_involvement, severity, |
| diagnosis_source, label_confidence, schema_version, |
| image_id, file_path, file_format, |
| modality, anatomy, device_technology, scan_protocol, |
| scan_x_mm, bscan_index, |
| image_height_px, image_width_px, axial_resolution_um, |
| has_segmentation, n_layers_visible, |
| fovea_x_norm, crt_um, choroid_thickness_um, |
| oct_footprint_bbox_fundus, oct_footprint_bbox_slo, |
| is_valid |
| ``` |
| |
| Plus per-image `image` bytes and per-cohort mask columns. |
| |
| ## Captions |
| |
| Each image has 5 captions (4 L1 variants + 1 L3 derived). Total 5.5M captions in `captions/`. |
| |
| ```python |
| from datasets import load_dataset |
| caps = load_dataset("mayberichard/zju-eye-pretrain", "captions_oct") |
| # join on image_id with the images config |
| ``` |
| |
| ## Licensing |
| |
| This dataset aggregates multiple sources with mixed licenses. See [LICENSE](LICENSE) for per-cohort license terms. Users are responsible for compliance with the original license of each cohort. |
| |
| **Private Shanghai Topcon data is included for research convenience.** Commercial use is prohibited. |
| |
| ## Citation |
| |
| If you use this dataset, please cite the original source for each cohort used (see DATASET_OVERVIEW.md). |
|
|
| ## Versioning & Updates |
|
|
| This dataset supports incremental updates. New cohorts can be added without touching existing data via additional shards in `data/<batch>/`. Schema migrations preserve old `*_v1.parquet` alongside new versions. |
|
|
