Photo by Amanda Dalbjörn on Unsplash

Samsung (OCT) Disease Prediction is a utility that facilitates the diagnosis of retinal disease from optical coherence tomography images. This project utilizes supervised and semi-supervised techniques to generate models trained at multi-class disease classification and exposes model inference through a collection of REST-ful API endpoints. The full project is deployable as a stand-alone, containerized application or on popular cloud-computing environments.

The OCT (Optical Coherence Tomography) is an imaging method used to diagnose the patient’s retinal health into four categories:

• Normal
• CNV
• DME
• DRUSEN

1. create a baseline: reproduce the SOTA classification accuracy by training a Deep CNN (Supervised Learning) We use Inception_V3 model for this part.
2. improve the classification accuracy over the baseline in part 1 using the self-supervised method described in simclr paper.
   • Learn the Unsupervised Features based on the Self-Supervision technique
   • Train a classification head on top of the learned Unsupervised Features to further improve the accuracy upon the established baseline.

• This tool can expedite the diagnosis of treatable diseases that can lead to blindness.

• By doing so, medication can be prescribed in time for the patients and prevent them from becoming blind.

• Additionally, the tool has the potential to be generalized in other applications of biomedical imaging including x-ray, MRI, and computer tomography)

To give some context, 30 million OCT scans are produced each year. Nearly 11 million people in the US alone will suffer from 1 of these diseases.

• The outcome is a support tool that provides retinal specialists with accurate and timely diagnosis of key pathology in OCT images.
• The OCT images can be used both for populations without proper access, and for those in overpopulated areas in order to reduce patient burden by using automated triage systems.

[Mendeley] (https://data.mendeley.com/datasets/rscbjbr9sj/2)

Model inference for supervised and semi-supervised learning methods are exposed through API endpoints using FastAPI. For convenience, the utility comes included with trained models -- an InceptionV3 neural network for supervised image classification and a ResNet-50 neural network utilizing the SimCLRv2 methodology -- available in the src/classifier/model directory. Furthermore, the repository includes a set of sample instances that were unseen from either model, all available in the src/corpus directory.

• Python 3.9+
• Docker

The included Dockerfile allows for building a minimal container environment to run and test the API.

To build a docker image from the project, run the following command from the root project directory:

docker build -t samsung-oct .

To create a new container, run the following command:

docker run -d --name <CONTAINER_NAME> -p 8000:8000 samsung-oct

NOTE The API may not be immediately available after running a container since the saved models need to be loaded.

The root endpoint to test whether the API service is running and functional.

GET /

None

{
    "service": "inference"
    "status": "online"
}

An endpoint to return a classification report from all instances in the corpus. Either the inceptionv3 or simclrv2 models can be used.

GET /classification_report/?model_name={{model_name}}

{
    "classification_report": {
        "0": {
            "precision": 0.6666666666666666,
            "recall": 1.0,
            "f1-score": 0.8,
            "support": 4
        },
        "1": {
            "precision": 0.375,
            "recall": 0.75,
            "f1-score": 0.5,
            "support": 4
        },
        "2": {
            "precision": 1.0,
            "recall": 0.5,
            "f1-score": 0.6666666666666666,
            "support": 4
        },
        "3": {
            "precision": 0.0,
            "recall": 0.0,
            "f1-score": 0.0,
            "support": 4
        },
        "accuracy": 0.5625,
        "macro avg": {
            "precision": 0.5104166666666666,
            "recall": 0.5625,
            "f1-score": 0.4916666666666667,
            "support": 16
        },
        "weighted avg": {
            "precision": 0.5104166666666666,
            "recall": 0.5625,
            "f1-score": 0.4916666666666667,
            "support": 16
        }
    }
}

An endpoint to return predictions for varying number of instances from the corpus.

NOTE: When stratification is used, sampling across each class will be conducted. The API will return the max number of instances in teach class if num_samples exceeds the total available instances in a given class in the corpus. When stratification is not used, random sampling across classes will be conducted.

GET /corpus_predict/?model_name={{model_name}}&num_samples={{num_samples}}&stratify={{stratify}}

{
    "predictions": [
        {
            "index": 0,
            "instance": "DRUSEN-224974-4.jpeg",
            "label": "DRUSEN",
            "prediction": 0
        }
    ]
}

Unit tests and API tests can be run after installing requirements:

python -m pytest src/tests/unit/test_utilities.py

python -m pytest src/tests/unit/test_utilities.py

• Asieh Harati
• Wolfgang Black
• Troy Jennings

• Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
• Big Self-Supervised Models are Strong Semi-Supervised Learners