데이터셋 탐색

The Rotterdam EyePACS AIROGS dataset (in full, so including train and test) contains 113,893 color fundus images from 60,357 subjects and approximately 500 different sites with a heterogeneous ethnicity.

This is a machine-learning-ready glaucoma dataset using a balanced subset of standardized fundus images from the Rotterdam EyePACS AIROGS train set. This dataset is split into training, validation, and test folders which contain 2500, 270, and 500 fundus images in each class respectively. Each training set has a folder for each class: referable glaucoma (RG) and non-referable glaucoma (NRG). Three versions of the same dataset are available with different standardization strategies: RAW - Resizing the source image to 256x256 pixels PAD - Padding the source image to a square image and then resizing it to 256x256 pixels. This method preserves the aspect ratio but the resultant image contains less usable information. CROP - Cropping black background in the fundus image, pad the resultant image to create a square image, and then resize to 256x256 pixels. This method preserves the aspect ratio and the resultant image contains the most usable information.

This is an improved machine-learning-ready glaucoma dataset using a balanced subset of standardized fundus images from the Rotterdam EyePACS AIROGS [1] set. This dataset is split into training, validation, and test folders which contain 4000 (~84%), 385 (~8%), and 385 (~8%) fundus images in each class respectively. Each training set has a folder for each class: referable glaucoma (RG) and non-referable glaucoma (NRG). Improvements from v1: Increased the image dimensions from 256x256 pixels to 512x512 pixels Swapped the image file format from JPG to PNG Added 3000 images from the Rotterdam EyePACS AIROGS dev set Readjusted train/val/test split Improved sampling from source dataset Drawbacks of Rotterdam EyePACS AIROGS: One of the largest drawbacks of the original dataset is the accessibility of the dataset. The dataset requires a long download, a large storage space, it spans several folders, and it is not machine-learning-ready (it requires data processing and splitting). The dataset also contains raw fundus images in their original dimensions; these original images often contain a large amount of black background and the dimensions are too large for machine learning inputs. The proposed dataset addresses the aforementioned concerns by image sampling and image standardization to balance and reduce the dataset size respectively. Origin: The images in this dataset are sourced from the Rotterdam EyePACS AIROGS [1] dataset, which contains 113,893 color fundus images from 60,357 subjects and approximately 500 different sites with a heterogeneous ethnicity; this impressive dataset is over 60GB when compressed. [1] EyePACS-AIROGS; https://airogs.grand-challenge.org/data-and-challenge/