OCTMNIST

Dataset Information

The OCTMNIST is based on a prior dataset of 109,309 valid optical coherence tomography (OCT) images for retinal diseases. The dataset is comprised of 4 diagnosis categories, leading to a multi-class classification task. We split the source training set with a ratio of 9:1 into training and validation set, and use its source validation set as the test set. The source images are gray-scale, and their sizes are (384−1,536)×(277−512). We center-crop the images and resize them into 1×28×28.

Task: multi-class

Labels:

0: choroidal neovascularization, 1: diabetic macular edema, 2: drusen, 3: normal

Samples:

• Train: 97477
• Validation: 10832
• Test: 1000

Experiment Parameter

Learning Rate: 3e-4

Training Epoch: 50

Convergence Epoch: 10