APTOS 2019 Diabetic Retinopathy Classification

Deep learning pipeline for detecting and grading diabetic retinopathy (DR) from retinal fundus images, built on the APTOS 2019 Blindness Detection Kaggle dataset. The project covers three progressively harder tasks:

1. Binary classification — DR vs. no DR.
2. Multiclass classification — DR severity grading (0–4) with self-supervised contrastive pre-training on an auxiliary ocular disease dataset.
3. Membership inference attack — a privacy analysis that probes whether a sample was part of the training set using Monte Carlo augmentation features.

All notebooks are written to run on Google Colab with the dataset stored on Google Drive and a GPU runtime.

Repository layout

.
├── binary_classifier.ipynb           # ResNet18 binary classifier (DR vs no DR)
├── multiclass_classifier.ipynb       # SimCLR-style pre-training + 5-class fine-tuning
├── APTOS_membership_inference.ipynb  # Monte Carlo membership inference attack
└── data/
    ├── train.csv                     # id_code, diagnosis (0–4) for 3,662 images
    └── test.csv                      # id_code for held-out Kaggle test set

The image folders (train_images/, test_images/) and the original Kaggle zip are gitignored — you need to download them yourself (see Setup).

Dataset

• Primary: APTOS 2019 Blindness Detection. 3,662 labeled training images graded on the international DR scale:
  • 0 — No DR
  • 1 — Mild
  • 2 — Moderate
  • 3 — Severe
  • 4 — Proliferative DR
• Auxiliary (pre-training): ODIR-5K Ocular Disease Recognition. A cropped subset of ~2,000 images is used for self-supervised pre-training of the backbone.

The label distribution is heavily imbalanced toward class 0, which motivated tackling the binary task first.

Methods

Shared preprocessing

• crop_retina removes the black border around each fundus image by thresholding the grayscale intensity and cropping to the non-black bounding box (with padding) — this prevents the model from learning border artifacts.
• Images are resized to 224×224 and normalized with ImageNet statistics.
• Training-time augmentations: horizontal flip, small rotations, and color jitter.

1. Binary classifier (binary_classifier.ipynb)

• Labels remapped to 0 (no DR) and 1 (any DR), which produces a roughly balanced dataset.
• Stratified 64/16/20 train/val/test split (random_state=42).
• ResNet18 (ImageNet-pretrained), final FC replaced with a 2-way head.
• Trained end-to-end with Adam (lr=1e-4), cross-entropy loss, 20 epochs, batch size 16.
• Best checkpoint selected by validation loss; reported metrics include accuracy, ROC AUC, sensitivity, and specificity.

2. Multiclass classifier (multiclass_classifier.ipynb)

A two-stage pipeline designed to mitigate class imbalance and limited labeled data:

Stage 1 — Self-supervised pre-training (SimCLR-style).

• Backbone: ResNet18 with the FC layer removed, followed by a 2-layer projection head (512 → 256 → 128).
• Each image produces two strongly augmented views (random resized crop, flips, color jitter, grayscale, Gaussian blur). The NT-Xent contrastive loss pulls views of the same image together and pushes other views apart.
• Trained on ~2,000 cropped images from the ODIR-5K dataset for 10 epochs, Adam (lr=1e-4), batch size 32.

Stage 2 — Supervised fine-tuning.

• Pre-trained backbone is loaded, parameters frozen, and a new Linear(512, 5) head is trained on the APTOS labels.
• Same 64/16/20 stratified split as the binary task, but stratified on the full 5-class label.
• Evaluation uses accuracy and Quadratic Weighted Kappa (the official APTOS competition metric), which penalizes predictions that are further from the true severity grade.

3. Membership inference attack (APTOS_membership_inference.ipynb)

A black-box privacy analysis of the fine-tuned multiclass model:

• Members = the training split used to fit the model; non-members = the val + test splits.
• For each image, T augmented views are passed through the trained model and Monte Carlo statistics are computed:
  • Mean / std of the true-class softmax probability
  • Mean / std of the predictive entropy
  • Mean / std of the per-sample cross-entropy loss
• A logistic regression attack model is trained on these 6 features to predict membership. Performance is reported via accuracy, ROC AUC, and the full ROC curve, with per-feature histograms comparing member vs. non-member distributions.

The intuition: trained-on images tend to have higher confidence, lower entropy, and lower loss under augmentation than unseen images, and a simple classifier can exploit that gap.

Setup

The notebooks were developed against Google Colab. To run them:

1. Get the data

   • Place a Kaggle API token at ~/.kaggle/kaggle.json (the first cells of each notebook handle uploading and permissions in Colab).
   • Download and unzip aptos2019-blindness-detection.zip into data/.
   • For Stage 1 pre-training, also download the ODIR-5K dataset and pre-crop a subset into data/cropped_pretrain_images/ using crop_retina.

2. Mount Drive (Colab) The notebooks expect the project at /content/drive/MyDrive/aptos_project with a data/ subfolder. Adjust PROJECT_DIR if running locally.

3. Dependencies (Colab has most of these pre-installed)

   • torch, torchvision
   • numpy, pandas, scikit-learn
   • opencv-python, Pillow
   • matplotlib, tqdm

4. Hardware A CUDA-capable GPU is strongly recommended. All notebooks auto-select cuda when available and fall back to cpu.

Reproducing results

Run the notebooks in this order:

1. binary_classifier.ipynb — sanity-check that the pipeline works end-to-end on the balanced binary task. Saves best_resnet18_binary.pth.
2. multiclass_classifier.ipynb — runs Stage 1 (pre-training, saves pretrain_model_weights.pth) and Stage 2 (fine-tuning, saves model_weights.pth), then evaluates on the held-out test split.
3. APTOS_membership_inference.ipynb — loads model_weights.pth and runs the attack pipeline.

Splits use random_state=42 throughout for reproducibility.

Notes and limitations

• The Kaggle test set has no public labels, so all reported metrics use an internal stratified split of the labeled training set.
• The membership inference attack uses a small sample (max_samples=100 per group by default) for speed — increase T and max_samples for tighter estimates.
• The pre-training stage assumes you have manually cropped and saved the ODIR-5K subset to data/cropped_pretrain_images/; the crop helper is the same crop_retina defined in each notebook.