File size: 2,539 Bytes
a4eef67 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | # 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. |