84 docs tagged with "relationship-derived_from"

705 optic disc-centred fundus photographs (396 normal + 309 glaucoma) from Hospital Clinico San Carlos. Expert-annotated binary glaucoma classification.

~113,893 color fundus images labelled as referable glaucoma (RG), no referable glaucoma (NRG), or ungradable. Large-scale, multi-ethnic, multi-site screening dataset.

Two thousand preprocessed fundus images compiled from six public sources for normal, diabetes, cataract, and age-related macular degeneration classification.

Augmented ODIR-5K fundus photographs for 8-class ocular disease classification: Normal, Diabetes, Glaucoma, Cataract, AMD, Hypertension, Myopia, Other. Preprocessing includes CLAHE and standard augmen

3662 fundus images from Aravind Eye Hospital, graded 0-4 for diabetic retinopathy severity.

Segmentation masks for the vascular arcade and optic nerve head derived from APTOS 2019 fundus images.

Fundus photographs from Bangladesh Eye Hospital for glaucoma detection. Includes optic cup/disc crops and vessel segmentation masks alongside binary glaucoma/normal labels.

16,266 color fundus images from 8,524 Brazilian patients with 14 disease labels (DR, AMD, glaucoma, drusen, others), image quality flags, and demographic attributes.

Semantic segmentation labels for 4,670 frames from 25 cataract surgery videos (CATARACTS challenge). 25 anatomy and instrument classes.

Extended phase/time labels for the Cataract-101 surgical-video dataset.

101 cataract surgery videos with 10-phase workflow annotations. Canonical older cataract benchmark.

Synthetic surgical-analysis instruction/chain-of-thought dataset derived from Cataract-1K frames.

1000 cataract surgery videos from multiple surgeons annotated for 10 surgical phases, instrument segmentation, and tool presence detection. First large-scale cataract surgical video dataset.

50 cataract surgery videos (>9 hours total) with frame-level annotations of 21 surgical tools. Earlier and larger sibling to Cataract-1K.

1,345 fundus images captured across multiple camera manufacturers with OD/OC segmentation + glaucoma classification labels.

28 fundus images with manual vessel segmentations (two annotators).

Composite fundus and OCT dataset with retinal layer, retinal lesion, and macular/glaucomatous disorder markings.

Observation-level source data, annotations, or signals. from Uses human retinal-image benchmarks; it contributes clinician disc tracings.

12522 fundus images with DR grading (0-5) and lesion-level segmentation annotations.

2,000 regular fundus images (500 patients × 2 fields × 2 eyes) plus 256 ultra-widefield fundus images labeled for DR severity (ICDR grades 0-4) and image quality assessment (gradable/ungradable). From

Fundus-image VQA dataset for diabetic macular edema derived from IDRiD and e-ophtha.

101 fundus images annotated for optic disc and cup segmentation by 4 clinicians. Train/test: 50/51.

Forty color fundus photographs from a diabetic retinopathy screening program, divided into 20 training and 20 test images, with vessel reference annotations and field of view masks.

110 OCT B-scans from 10 DME subjects with 8 layer boundaries and fluid region annotations by 2 clinicians.

463 color fundus images with pixel-level segmentation: 82 with exudate (EX) + 381 with microaneurysm (MA) annotations. Standard DR lesion-segmentation benchmark.

Precomputed image embeddings for BRSET and mBRSET to support efficient ophthalmic AI research without raw-image redistribution.

Original and augmented eye-disease image dataset covering retinitis pigmentosa, retinal detachment, pterygium, myopia, macular scar, glaucoma, disc edema, diabetic retinopathy, central serous choriore

~88,000 fundus images graded 0–4 for DR severity. Largest public DR dataset; competition images from EyePACS clinics.

Quality labels for 28,792 EyePACS fundus images, graded as good, usable, or reject and divided into the original EyePACS train and test partitions.

800 high-resolution (2048x2048) fundus images with pixel-wise vessel segmentation. Covers normal, DR, AMD, glaucoma.

Five-domain optic disc/cup segmentation benchmark composed from REFUGE, Drishti-GS, ORIGA, RIGA, and related sources.

Fundus/UWF image-report dataset derived from DeepDRiD and OUWFD-style resources for report-generation research.

1020 fundus images with binary glaucoma labels (697 normal, 323 glaucoma), plus optic disc/cup annotations.

300 paired fundus + 3D OCT volumes. Glaucoma grading into Normal / Early / Advanced plus OD/OC segmentation and fovea location.

Revised glaucoma reasoning reports paired with the documented source fundus cases.

300 circumpapillary OCT images. RNFL/GCIPL/choroid layer segmentation plus binary glaucoma classification.

1,000 patients with OCT RNFLT maps (225×225), visual field measurements, and demographics for glaucoma detection (binary) and longitudinal progression forecasting (6 definitions). First public glaucom

Synthetic glaucoma/normal fundus images with documented human source collections.

Extended HRF annotations for optic disc, cup, retinal vessels, and alpha/beta peripapillary zones.

45 high-resolution fundus images (healthy/DR/glaucoma) with manual vessel segmentation.

516 fundus images with DR grade (0-4), macular edema grade (0-2), and pixel-level lesion segmentation for 81 images.

Two COCO-format instance-segmentation datasets derived from cataract surgery videos, covering instruments and anatomical structures.

OCT images for intraretinal cystoid-fluid segmentation, partly derived from public OCT DME sources with expert-selected masks.

Human cataract-surgery video frames with iris and pupil pixel masks.

9,939 color fundus images from Japanese patients with DR grading labels (Davis grading scale).

1,000 fundus images spanning 39 ophthalmic disease categories from the Joint Shantou International Eye Center. Used for multi-class fundus disease classification.

~84,000 retinal OCT B-scan images across 4 classes: CNV, DME, DRUSEN, NORMAL. Train: ~83,484 / Test: 1000.

11,760 fundus images with glaucoma classification labels and ophthalmologist-derived attention maps. Largest public glaucoma dataset with attention ground truth.

Processed Cataract-1K surgical-frame dataset for segmentation/object-detection workflows.

Composite multimodal ophthalmology benchmark with multi-granular anatomical, diagnostic, staging, demographic, and text annotations.

5,164 fundus images from 1,291 patients captured with the Phelcom Eyer handheld smartphone-based fundus camera. Labels for DR grading + clinical/demographic prediction.

1748 fundus images with DR grading (0-3 Retinopathy Grade) and macular edema risk (0-2).

Ophthalmology-specific multimodal reasoning dataset built from 45 public datasets. Chain-of-thought reasoning traces for retinal VQA.

Multi-modal DR dataset combining color fundus photographs (CFP), OCT B-scans, and ultra-widefield (UWF) fundus images, annotated for DR detection and severity grading. Published in Nature Scientific D

58,036 fundus + 45,923 OCT images assembled for multi-disease classification (8 classes) with cross-modal distillation. Sourced from multiple public ophthalmic datasets.

2,208 color fundus images with 20-class multi-label disease annotations.

Augmented DRIVE, STARE, and CHASE_DB1 vessel-segmentation images for out-of-distribution robustness evaluation.

500 subjects with OCTA volumes, vessel segmentation, FAZ (foveal avascular zone) annotations, and layer segmentation. Largest public OCTA dataset.

2,000+ OCT images labeled for 7 conditions: AMD, DME, ERM, NO (normal), RAO, RVO, VID.

500 OCT images: NORMAL (206), AMD (50), CSC (128), DR (59), MH (57). High-resolution B-scans for 5-class classification.

8000 patients (16000 fundus images, left + right eye) with 8 disease labels for multi-label ocular disease classification.

3,859 OCT B-scan images (125 eyes, 119 patients) for macular hole segmentation. Each PNG is side-by-side: left half raw B-scan, right half colour-coded mask for 4 classes: Retina, Macular Hole, Intrar

1200 fundus images for pathologic myopia classification and optic disc / lesion segmentation. 50 % PM / 50 % non-PM.

488 fundus images from 244 patients (both eyes). Expert OD/OC segmentation, glaucoma stage, IOP, and clinical metadata.

757 color fundus images from a Paraguayan cohort with 7-class DR grading labels. Adds Latin-American representation.

Fundus-photography dataset for retinal artery occlusion diagnosis, based on web-derived public data and public fundus datasets.

1200 fundus images from REFUGE1 with optic disc/cup segmentation masks annotated by multiple expert raters.

2000 fundus images for glaucoma classification, optic disc/cup segmentation, and fovea localisation. Extends REFUGE1.

Retinal vessel analysis benchmark with vessel masks, artery/vein masks and skeletons, bifurcation points, vascular trees, and abnormality annotations.

~24,000 retinal OCT images across 8 disease classes: AMD, BRAO, BRVO, CSC, CRAO, CRVO, DME, MH.

Auxiliary RFMiD 2.0 retinal fundus dataset spanning 51 disease categories.

3200 fundus images annotated for 45 retinal conditions. Used for multi-label disease classification.

750 fundus images with optic disc and optic cup segmentations from 6 ophthalmologists per image. Multi-rater benchmark.

RIGA/MESSIDOR-derived benchmark for optic disc and cup segmentation domain adaptation.

40 fundus images (derived from DRIVE) with artery/vein annotations and vessel-tree structural labels.

100 fundus images with microaneurysm annotations from the Retinopathy Online Challenge.

~12,000 fundus images aggregated from 19 public glaucoma datasets with standardized disc/cup/vessel channels and unified labels. CC0 — fully public domain.

400 macular OCT volumes; predict glaucoma Mean Deviation (MD, dB) from 24-2 Humphrey visual field test. Scalar regression task.

400 macular OCT volumes; predict 52-point Humphrey 24-2 visual field sensitivity map (0–100 dB per point). Multi-output regression.

20 fundus images with manual vessel segmentation (two annotators).

1,219 color fundus images with DR severity grading + pixel-level exudate segmentation masks. Multi-center (SUSTech + Sun Yat-sen University).

Synthetic OCT images for the four Kermany diagnostic classes.

A multilabel fundus classification challenge with 3,435 labeled training images and 350 test images covering six disease groups and normal findings.

The version-pinned public deposit contains 18,735 ophthalmic image-text benchmark rows across CFP, external-eye, FFA, OCT, and RetCam subsets.