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OCT Classification

Dataset Context

Retinal optical coherence tomography (OCT) is an imaging technique used to capture high-resolution cross sections of the retinas of living patients. Approximately 30 million OCT scans are performed each year, and the analysis and interpretation of these images takes up a significant amount of time (Swanson and Fujimoto, 2017)

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(A) (Far left) choroidal neovascularization (CNV) with neovascular membrane (white arrowheads) and associated subretinal fluid (arrows). (Middle left) Diabetic macular edema (DME) with retinal-thickening-associated intraretinal fluid (arrows). (Middle right) Multiple drusen (arrowheads) present in early AMD. (Far right) Normal retina with preserved foveal contour and absence of any retinal fluid/edema.

Dataset Content

The dataset is organized into 2 folders (train, test) and contains subfolders for each image category (NORMAL,CNV,DME,DRUSEN). There are 84,495 X-Ray images (JPEG) and 4 categories (NORMAL,CNV,DME,DRUSEN). You can load OCT Dataset by click https://data.mendeley.com/datasets/rscbjbr9sj/2

Images are labeled as (disease)-(randomized patient ID)-(image number by this patient) and split into 4 directories: CNV, DME, DRUSEN, and NORMAL.

Structure

../OCT_Classification
├── checkpoint
│   ├── oct2017_classification_finetune_resnet_10epochs.pth
│   └── PDBL_with_finetune_on_resnet.pkl
├── models
│   ├── pdbl.py
│   ├── resnet.py
│   ├── utils.py
│   └── __init__.py
├── OCT_Dataset
│   ├── OCT2017
│   │   ├── test
│   │   └── train
│   └── Processed_OCT2017
│       ├── test
│       └── train
├── utils
│   ├── dataset.py
│   ├── process.py
│   └── __init__.py
└── trainer.py

The role of each file or folder is as follows:

checkpoint : save the parameters of models.
models : architecture of the classifier models which is restnet50 with PDBL.
OCT_Dataset: OCT2017 is raw data and Processed_OCT2017 is processed data.
utils: make Dataset and preprocess the data.
trainer.py: train and test model.

Requirements

matplotlib==3.3.4
numpy==1.20.1
opencv_contrib_python==4.5.4.60
pandas==1.2.4
scikit_learn==1.1.3
torch==1.9.0
torchvision==0.13.0
tqdm==4.59.0

Usage

Installation

Download the repository.

git clone https://github.com/aishangcengloua/OCT_Classification.git

Install python dependencies.

pip install -r requirements.txt

Training and Inference

python trainer.py --save_dir checkpoint --train_dir OCT_Dataset/Processed_OCT2017/train --val_dir OCT_Dataset/Processed_OCT2017/test --n_classes 4 --train_model True