This release is a pre-release and may not be stable for production use.
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
Medical-AI is a AI framework for rapid prototyping of AI for Medical Applications.
Installation
pip install medicalai
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
1 maintainer
Metadata
1 maintainer
Release files for medicalai 1.2.8rc0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| medicalai-1.2.8rc0.tar.gz | 44.6 kB | Details |
Release files / medicalai-1.2.8rc0.tar.gz
| Download URL | medicalai-1.2.8rc0.tar.gz |
|---|---|
| Size | 44.6 kB |
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