| # OCTID Processed Dataset |
|
|
| Processed version of the OCTID retinal OCT dataset prepared for |
| research experiments comparing Swin Transformer architectures. |
|
|
| ## Dataset Summary |
|
|
| - Dataset: OCTID |
| - Total images: 572 |
| - Number of classes: 5 |
| - Image size: 224 x 224 |
| - Image mode: RGB |
| - Image format: JPEG |
|
|
| ## Classes |
|
|
| | Class | Images | |
| |---|---:| |
| | Normal | 206 | |
| | AMD | 55 | |
| | CSR | 102 | |
| | DR | 107 | |
| | MH | 102 | |
| | Total | 572 | |
|
|
| ## Preprocessing |
|
|
| The preprocessing pipeline consists of: |
|
|
| 1. Aspect-ratio-preserving resize |
| 2. Center crop to 224 x 224 |
| 3. Non-Local Means denoising |
| 4. CLAHE enhancement |
|
|
| ImageNet normalization is intentionally not stored in the images. |
| It will be applied during the PyTorch training pipeline. |
|
|
| ## Experimental Splits |
|
|
| Five repeated stratified splits are provided. |
|
|
| Each split contains approximately: |
|
|
| - 50% training |
| - 20% validation |
| - 30% testing |
|
|
| Seeds: |
|
|
| - 42 |
| - 52 |
| - 62 |
| - 72 |
| - 82 |
|
|
| The same split files should be used when comparing different |
| models to ensure a fair experimental comparison. |
|
|
| ## Repository Structure |
|
|
| images/ |
| splits/ |
| metadata/ |
| README.md |
|
|
| ## Intended Use |
|
|
| This processed dataset is intended for research experiments |
| involving retinal OCT image classification and comparison of |
| deep learning models, including Swin Transformer and custom |
| Swin Transformer architectures. |
|
|
| The provided split files should be used consistently across |
| models to maintain a fair comparison. |
|
|
| ## Image Preprocessing |
|
|
| The saved images have already undergone: |
|
|
| - Aspect-ratio-preserving resize |
| - Center crop |
| - Non-Local Means denoising |
| - CLAHE enhancement |
|
|
| ImageNet normalization is NOT included in the saved JPEG files. |
|
|
| For model training, ImageNet normalization should be applied |
| after loading the image and converting it to a tensor. |
|
|
| ## Reproducibility |
|
|
| The repository includes five stratified train/validation/test |
| splits and the experiment configuration used during dataset |
| preparation. |
|
|
| These files are provided so that the same experimental partitions |
| can be reused across different models. |
|
|
| ## Dataset Integrity |
|
|
| - Original images checked: 572 |
| - Corrupted original images: 0 |
| - Original duplicate files: 0 |
| - Processed images: 572 |
| - Missing processed images: 0 |
| - Processed duplicate files: 0 |
| - Processed image size: 224 x 224 |
| - Processed image mode: RGB |
|
|
| ## License and Citation |
|
|
| This repository contains processed research data derived from OCTID. |
|
|
| Please consult the original OCTID dataset/source for its licensing, |
| citation requirements, and redistribution conditions before using |
| or publishing this dataset. |
