This repository contains supplementary code for linkEnhancing Retinal Disease Classification from OCTA Images via Active Learning Techniques, published in Data Engineering in Medical Imaging (DEMI) workshop at MICCAI 2024. In this work, we analyze active learning as a data subset technique to improve classification performance on OCTA images.
Figure: Schematic diagram of active learning pipeline, where $`\mathcal{D}_{train, val}`$ are the train and validation set, respectively, X is an image, s is the uncertainty score, and k is the number of images to move into the train set after each active learning iteration.We utilize the OCTA500 dataset, which contains images of healthy, age-related macular degeneration (AMD), chorodial neovascularization (CNV), and diabetic retinopathy (DR) retinas. More details can be found link here
Figure: Comparison of OCT and OCTA data for Normal, CNV, DR, and AMD eyes.Training Method | Acc | F1 |
---|---|---|
Unbalanced | .5139 | .4864 |
Inverse Frequency Class Weighting | .4956 | .4571 |
Random Undersampling | .4482 | .3334 |
Oversampling (AutoAugment) | .4178 | .4136 |
Oversampling (AugMix) | .4647 | .4503 |
Least Confident Sampling | .7313 | .6285 |
Entropy Sampling | .7188 | .6187 |
Margin Sampling | .7282 | .6262 |
Ratio Sampling | .7688 | .7116 |