Ocular Disease Intelligent Recognition ODIR-5K

ODIR-5K (3 files)
ODIR-5K_Testing_Images.7z 171.42MB
ODIR-5K_Training_Annotations(Updated)_V2.xlsx 268.20kB
ODIR-5K_Training_Images.7z 1.13GB
Type: Dataset

title= {Ocular Disease Intelligent Recognition ODIR-5K},
keywords= {},
author= {},
abstract= {We collected a structured ophthalmic database of 5,000 patients with age, color fundus photographs from left and right eyes and doctors' diagnostic keywords from doctors (in short, ODIR-5K). This dataset is ‘‘real-life’’ set of patient information collected by Shanggong Medical Technology Co., Ltd. from different hospitals/medical centers in China. In these institutions, fundus images are captured by various cameras in the market, such as Canon, Zeiss and Kowa, resulting into varied image resolutions. Patient identifying information will be removed. Annotations are labeled by trained human readers with quality control management. They classify patient into eight labels including normal (N), diabetes (D), glaucoma (G), cataract (C), AMD (A), hypertension (H), myopia (M) and other diseases/abnormalities (O) based on both eye images and additionally patient age. The publishing of this dataset follows the ethical and privacy rules of China. Table 1 shows one record from ODIR-5K dataset.

The 5,000 patients in this challenge are divided into training, off-site testing and on-site testing subsets. Almost 4,000 cases are used in training stage while others are for testing stages (off-site and on-site). Table 2 shows the distribution of case number with respect to eight labels in different stages. Note: one patient may contains one or multiple labels.



terms= {},
license= {},
superseded= {},
url= {https://odir2019.grand-challenge.org/}

10 day statistics (2 downloads)

Average Time 24 mins, 24 secs
Average Speed 888.00kB/s
Best Time 11 mins, 43 secs
Best Speed 1.85MB/s
Worst Time 37 mins, 06 secs
Worst Speed 584.22kB/s