Edge Analytics in Healthcare #

Cancer Predicition System is an ML-based application stack with the goal to assist in cancer risk assessment by predicting the likelihood of developing a cancer prior to occurence of the disease. The image below illustrates the general architecture of the Cancer Prediction System.

The Cancer Prediction System uses CT scan images in the Digital Imaging and Communications in Medicine (DICOM) standard. The system is designed to be deployed in a microservices architecture and is divided into four interfaces:

The Cancer Imaging Archive (TCIA) is a service that de-identifies and hosts a large archive of medical images of cancer accessible for public download.

Note

• Albertina, B., Watson, M., Holback, C., Jarosz, R., Kirk, S., Lee, Y., … Lemmerman, J. (2016). Radiology Data from The Cancer Genome Atlas Lung Adenocarcinoma [TCGA-LUAD] collection. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2016.JGNIHEP5

• Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper)

The Cancer Prediction System uses the subset of TCIA images that were featured in a Kaggle competition. This dataset is designed to allow testing of different methods for examining trends in CT image data associated with using contrast and patient age. It consists of 475 series of CT images from 69 participants where valid age, modality, and contrast tags could be found.

The Cancer Prediction System aims to assist a doctor in identifying the probability of cancer in a patient based on a CT image. That is, the Cancer Prediction System must classify a CT image as indicative of cancer or not indicative of cancer. This classification problem can be a challenge for traditional solution methods.

To approach this problem, the Cancer Prediction System uses a predictive model created with a convolutional neural network (CNN). CNNs have been shown to achieve higher accuracy in detecting and segmenting specific objects in images. In this solution, you use a CNN architecture consisting of three convolutional layers and two dense layers with the RMSprop optimizer and binary cross-entropy loss function.

CNNs must be trained before they can be useful. CNN training can involve unsupervised learning or supervised learning. Unsupervised learning is indicated when the categories are unknown or when the training dataset is not already categorized (that is, labeled or tagged). In this case, you have two categories already defined ('cancer' and 'not cancer') and a categorized training dataset. So, the Cancer Prediction System uses supervised learning to train its CNN.

The training process is detailed as follows:

No machine learning model is perfect. It is quite common to retrain CNNs on new data, including updated labels for misclassified images. The Cancer Prediction System allows for retraining and automates the process with Kubeflow Pipelines.

Cancer Prediction System is built on top of Flask, a lightweight Web application framework. You can find the code under application inside your project directory, which you created earlier by cloning the project repository. The solution contains two separate applications: one for the doctor (under doctor_app) and one the radiologist (under lab_tech).

/application
-- doctor_app/
   -- app.py
   -- Dockerfile
   -- classification-model.h5
   -- prediction-model.h5
   -- requirements.txt
   -- static/
      -- styles/
         -- css/
         -- js/
   -- template/
      -- base.html
      -- gallery.html
      -- predict.html
      -- retrain.html
      -- upload.html

-- lab_tech/
   -- app.py
   -- Dockerfile
   -- classification-model.h5
   -- adjust.py
   -- requirements.txt
   -- static/
      -- styles/
         -- css/
         -- js/
   -- template/
      -- base.html
      -- predict.html
      -- send.html
      -- upload.html

6.4 Configure external access #

 

The final step in configuring the Cancer Prediction System is to enable external access.

With SUSE Rancher, you can configure load balancers or ingress controllers to redirect service requests. Load balancers can only handle one IP address per service, while ingress works with one or more ingress controllers to dynamically route service requests.

For real-world use, it is recommended that you configure your cluster with an ingress.

Note

Ingress and ingress controllers residing in RKE-launched clusters are powered by NGINX.

Below is a sample ingress resource for the Cancer Prediction System:

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: ingress-cancerprediction
spec:
  rules:
  - host: "cancerpred.example.com"
    http:
      paths:
      - path: /doctor
        pathType: Prefix
        backend:
          service:
            name: doctor-svc
            port:
              number: 443
      - path: /labtech
        pathType: Prefix
        backend:
          service:
            name: labtech-svc
            port:
              number: 443

Tip

You must have properly configured DNS resolution to use a domain name (such as 'cancerpred.example.com') for application access.

The Cancer Prediction System has two primary interfaces, the Lab Technician Interface and the Doctor’s Dashboard.

Advanced analytics and machine learning are helping deliver better healthcare outcomes. By leveraging SUSE Rancher and a Kubernetes-native approach, life-saving applications can be deployed everywhere - in the data center, in the cloud, and even at the edge.

Continue your learning journey:

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