--- 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 Unified multi-source ophthalmological imaging dataset for foundation model pretraining and downstream tasks. **1.18M images** spanning **57 cohorts** with a **strict 41-column unified manifest schema**. ## Composition **1,179,458 images · 57 cohorts · 8 modalities**, under one strict 41-column unified manifest schema. Stored as HF parquet shards in `data/{private_topcon, public_fundus, public_oct, public_new}/`; loadable by batch or by single cohort (57 configs, see YAML above). | modality | images | cohorts | |---|---:|---:| | OCT B-scan (`oct_bscan`) | 839,729 | 22 | | color fundus (`fundus_color`) | 284,685 | 28 | | SLO (`slo_gray`) | 36,027 | 2 | | ultra-widefield fundus (`uwf_fundus`) | 13,321 | 4 | | OCT-angiography en-face (`octa_enface`) | 1,500 | 1 | | en-face OCT (`oct_enface`) | 1,500 | 1 | | fluorescein angiography (`fluorescein_angiography`) | 1,376 | 1 | | slit-lamp (`slit_lamp`) | 1,320 | 1 | > Two cohorts are multi-modal and are counted under each modality they contribute: `shanghai_topcon` (private; OCT + SLO + fundus) and `gamma_multimodal` (OCT + fundus). Per-modality cohort counts therefore sum to 60 over 57 unique cohorts. See [DATASET_OVERVIEW.md](DATASET_OVERVIEW.md) for per-cohort devices, regions, masks and demographics. ### OCT B-scan — 839,729 (22 cohorts) | cohort | images | | cohort | images | |---|---:|---|---|---:| | `shanghai_topcon` (private) | 352,343 | | `srinivasan_2014` | 3,231 | | `octa500` | 119,600 | | `aroi` | 3,072 | | `kermany` | 109,309 | | `amd_sd` | 3,049 | | `olives` | 63,489 | | `rvome_oct` | 3,012 | | `gamma_multimodal` | 51,200 | | `mmrdr_oct` | 2,936 | | `areds2` | 38,382 | | `octdl` | 1,877 | | `uestc` | 35,280 | | `chiu_dme_2015` | 610 | | `c8` | 24,000 | | `octid` | 572 | | `neh_ut_2021` | 16,810 | | `thoct1800` | 96 | | `retouch` | 6,936 | | `glaucoma` | 49 | | `oimhs` | 3,859 | | `sparsity_sdoct_2012` | 17 | ### Color fundus — 284,685 (28 cohorts) | cohort | images | | cohort | images | |---|---:|---|---|---:| | `eyepacs_combo_dr_aug` | 143,668 | | `g1020_glaucoma` | 1,020 | | `mfiddr_dr` | 34,452 | | `fives_vessel` | 800 | | `shanghai_topcon` (private) | 30,714 | | `acrima_glaucoma` | 705 | | `brset_multidisease` | 16,266 | | `origa_glaucoma` | 650 | | `ddr_dr` | 12,522 | | `idrid` | 597 | | `mmrdr_cfp` | 11,112 | | `papila_glaucoma` | 488 | | `airogs_glaucoma` | 9,540 | | `rimone_glaucoma` | 485 | | `odir_multidisease` | 6,392 | | `adam_amd` | 400 | | `lag_glaucoma` | 4,854 | | `gamma_multimodal` | 200 | | `rfmid_multidisease` | 3,200 | | `drishti_glaucoma` | 101 | | `fgadr_dr` | 1,842 | | `hrf_vessel` | 45 | | `messidor2_dr` | 1,744 | | `stare_vessel` | 40 | | `deepdrid_dr` | 1,600 | | `chasedb1_vessel` | 28 | | `refuge2_disc_cup` | 1,200 | | `drive_vessel` | 20 | ### Ultra-widefield fundus — 13,321 (4 cohorts) | cohort | images | task | |---|---:|---| | `mmrdr_uwf` | 10,388 | DR + 7 lesions | | `uwf_tumor` | 2,031 | retinal tumor | | `uwf_disease` | 700 | 7-class (AMD / DR / PM / RD / RVO / uveitis / normal) | | `deepdrid_uwf` | 202 | DR grading | ### SLO — 36,027 (2 cohorts) Confocal scanning-laser ophthalmoscopy, en-face grayscale (both near-infrared). - `shanghai_topcon` (private) — 35,985 — Topcon DRI OCT Triton near-IR SLO localizer. - `ravir_av` — 42 — Heidelberg Spectralis IR-SLO (815 nm), with artery/vein segmentation masks. ### Smaller modalities (1 cohort each) | modality | images | cohort | task | |---|---:|---|---| | OCT-angiography en-face | 1,500 | `octa500_octa_enface` | vascular maps (3 depth projections) + FAZ/vessel masks | | en-face OCT | 1,500 | `octa500_oct_enface` | OCT depth projections | | fluorescein angiography | 1,376 | `iovs_fa` | leakage (DME) + late-phase masks | | slit-lamp | 1,320 | `nuclear_cataract` | nuclear cataract | > OCT-public quality screen: B-scans with height < 256 px were removed — the `nyu_poag` cohort entirely (56,576) plus 1,891 sub-256px from `thoct1800`/`octdl`. ## Quick Start ```python from datasets import load_dataset, concatenate_datasets, Image # === Load by batch (4 batches) === 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") ds_new = load_dataset("mayberichard/zju-eye-pretrain", "public_new") # 32 cohorts, 8 modalities # === Load by single cohort (57 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 all 4 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()), ds_new["train"].select_columns(shared).cast_column("image", Image()), ]) # 1.18M images total, mask cols dropped (use per-batch load if you need masks) # === Streaming for big training runs (avoids downloading all ~340 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 (v2 — disease/lesion-centric) Captions were redesigned (2026-06, **replacing v1**) to be disease/lesion-centric and EyeDiff-style, after early training showed the old metadata-heavy captions hurt prompt-following. Text structure (comma-separated): ``` {modality}, {region}, {diagnosis[+severity]}, {lesions...} ``` Each image has **1–3 captions** at increasing specificity (`level` column), de-duplicated: | level | example | |---|---| | `short` | `OCT B-scan, diabetic macular edema` | | `medium` | `color fundus, moderate non-proliferative diabetic retinopathy` | | `dense` | `OCT B-scan, macula, neovascular age-related macular degeneration, choroidal neovascularization` | - Disease/severity/lesion terms are standardized through a controlled vocabulary (`code/disease_dict.py`). - Acquisition metadata (device, dataset name, quality score, slice index, bbox, µm thickness, eye) is **kept out of the prompt** — it lives in the image manifest. - **Training tip:** sample one tier per image per step, and replace the caption with `""` ~10% of the time for classifier-free guidance (dropout is a loader concern; empty captions are not stored). - Private Topcon OCT currently carries only `modality, region` (no disease label yet) — disease pseudo-labels are pending. Files in `captions/`: `captions_v2.parquet` (private), `public_captions_v2.parquet`, `oct_public_captions_v2.parquet`, `public_new_captions_v2.parquet`, plus per-cohort `*_captions_v2.parquet`. Total ~2.25M caption rows (100% image coverage; `short` on every image, `medium`/`dense` conditional by modality). Join on `image_id`. ```python import pandas as pd caps = pd.read_parquet("hf://datasets/MaybeRichard/zju-eye-pretrain/captions/oct_public_captions_v2.parquet") # join on image_id with the images config ``` ## Denoising / Super-resolution test set (separate — NOT part of pretraining) A standalone **paired OCT denoising** test set lives under `denoising_test/`, kept **separate** from the pretraining cohorts (its own config, `split=test`). It is **1,735 paired** 640×640 grayscale OCT B-scans (noisy input ↔ clean target; original `.tif` bytes). Do not mix it into the pretraining configs. | column | type | note | |---|---|---| | `id` | int | pair id (1–1735) | | `noisy` | bytes | noisy OCT B-scan (`.tif`) | | `clean` | bytes | clean ground-truth (`.tif`) | | `noisy_filename` / `clean_filename` | str | original names | ```python from datasets import load_dataset from datasets import Image ds = load_dataset("MaybeRichard/zju-eye-pretrain", "denoising_test", split="test") ds = ds.cast_column("noisy", Image()).cast_column("clean", Image()) # PIL decodes the TIFF bytes ``` ## 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//`. **Captions were upgraded to v2 (disease/lesion-centric) in 2026-06; the old v1 caption files were replaced.** **Quality screening (2026-06):** OCT-public images with width or height < 256 px were removed (**58,467 imgs**): the `nyu_poag` cohort entirely (56,576, all 64×128), most of `thoct1800` (1,704, height 149) and 187 from `octdl`. A full pure-black scan found 0 (low-mean "dark" frames are normal OCT and were kept). OCT-public is now **18 cohorts / 430,238 images** (was 19 / 488,705); private and fundus unchanged.