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MedicalAI

Medical-AI is a AI framework for rapid protyping for Medical Applications

Documentation: https://aibharata.github.io/medicalAI/

Source Code: https://github.com/aibharata/medicalai

Youtube Tutorial: https://www.youtube.com/V4nCX-kLACg

Downloads Downloads Documentation Status Gitter

Medical-AI is a AI framework for rapid prototyping of AI for Medical Applications.

Installation

pip install medicalai
## Requirements Python Version : 3.5-3.7 (Doesn't Work on 3.8 Since Tensorflow does not support 3.8 yet.

Dependencies: Numpy, Tensorflow, Seaborn, Matplotlib, Pandas

NOTE: Dependency libraries are automatically installed. No need for user to install them manually.

Usage

Getting Started Tutorial: Google Colab Google Colab Notebook Link

Importing the Library

import medicalai as ai

Using Templates

You can use the following templates to perform specific Tasks

Load Dataset From Folder

Set the path of the dataset and set the target dimension of image that will be input to AI network.

trainSet,testSet,labelNames =ai.datasetFromFolder(datasetFolderPath, targetDim = (96,96)).load_dataset()
- trainSet contains 'data' and 'labels' accessible by trainSet.data and trainSet.labels
- testSet contains 'data' and 'labels' accessible by testSet.data and testSet.labels
- labelNames contains class names/labels

Check Loaded Dataset Size

print(trainSet.data.shape)
print(trainSet.labels.shape)

Run Training and Save Model

trainer = ai.TRAIN_ENGINE()
trainer.train_and_save_model(AI_NAME= 'tinyMedNet', MODEL_SAVE_NAME='PATH_WHERE_MODEL_IS_SAVED_TO', trainSet, testSet, OUTPUT_CLASSES, RETRAIN_MODEL= True, BATCH_SIZE= 32, EPOCHS= 10, LEARNING_RATE= 0.001)

Plot Training Loss and Accuracy

trainer.plot_train_acc_loss()

Generate a comprehensive evaluation PDF report

trainer.generate_evaluation_report()

PDF report will be generated with model sensitivity, specificity, accuracy, confidence intervals, ROC Curve Plot, Precision Recall Curve Plot, and Confusion Matrix Plot for each class. This function can be used when evaluating a model with Test or Validation Data Set.

Explain the Model on a sample

trainer.explain(testSet.data[0:1], layer_to_explain='CNN3')

Loading Model for Prediction

infEngine = ai.INFERENCE_ENGINE(modelName = 'PATH_WHERE_MODEL_IS_SAVED_TO')

Predict With Labels

infEngine.predict_with_labels(testSet.data[0:2], top_preds=3)

Get Just Values of Prediction without postprocessing

infEngine.predict(testSet.data[0:2])

Alternatively, use a faster prediction method in production

infEngine.predict_pipeline(testSet.data[0:1])

Advanced Usage

Code snippet for Training Using Medical-AI

## Setup AI Model Manager with required AI. 
model = ai.modelManager(AI_NAME= AI_NAME, modelName = MODEL_SAVE_NAME, x_train = train_data, OUTPUT_CLASSES = OUTPUT_CLASSES, RETRAIN_MODEL= RETRAIN_MODEL)

# Start Training
result = ai.train(model, train_data, train_labels, BATCH_SIZE, EPOCHS, LEARNING_RATE, validation_data=(test_data, test_labels), callbacks=['tensorboard'])

# Evaluate Trained Model on Test Data
model.evaluate(test_data, test_labels)

# Plot Accuracy vs Loss for Training
ai.plot_training_metrics(result)

#Save the Trained Model
ai.save_model_and_weights(model, outputName= MODEL_SAVE_NAME)

Automated Tests

To Check the tests

    pytest

To See Output of Print Statements

    pytest -s

Author

Dr. Vinayaka Jyothi