Datasets:

MaybeRichard
/

zju-eye-pretrain

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 for full details per cohort (devices, regions, masks, demographics).

Quick Start

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/.

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 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.