23 docs tagged with "kaggle"

3,049 OCT B-scan images (1,140×380 px) from 138 wet AMD patients (156 eyes) with pixel-level annotations for 5 lesion classes: Subretinal Fluid (SRF), Intraretinal Fluid (IRF), Ellipsoid Zone Continui

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.

35,126 fundus images for 5-class diabetic retinopathy grading (No DR: 25,810 / Mild: 2,443 / Moderate: 5,292 / Severe: 873 / Proliferative: 708). Available via Kaggle.

601 fundus images in 4 classes: Normal, Cataract, Glaucoma, and Retina Disease. Intended for ocular disease classification.

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

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

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

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

HD-OCT scans and clinical outcome variables for macular-hole visual-improvement prediction after surgery.

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

Corneal map images for three-class keratoconus detection.

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

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

Russian-English ophthalmology sentence-pair and glossary dataset for translation and LLM evaluation.

6,004 pediatric RetCam fundus images from 188 newborns in Ostrava (Czech Republic), annotated for retinopathy of prematurity (ROP) screening + classification.

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

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

485 fundus images (313 normal, 172 glaucoma) with optic disc region of interest crops for deep learning.

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

Fundus photographs for ocular toxoplasmosis detection: active toxoplasmosis, inactive (scarred) lesions, and normal. ~412 images total (adult + pediatric cases).

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

Visual-field and psychophysics experiment data for scotoma-detection comparisons.