aibharata / medicalai

Medical-AI is a AI framework specifically for Medical Applications https://aibharata.github.io/medicalAI/

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medicalai's Issues

Medicalai ErrorStoppping Not Working

Hi @aibharata

When we want to stop the training early with medicalai via a callback called EarlyStopping, it gives an error at line 209 in the file "medicalai/chief/core.py".
How should the data be defined here, because it is desired to behave like a string first and then a dictionary.
Can you share sample data?

Error line

if bestModelCond is not None and bestModelCond.lower() != 'default':
print('\n[INFO]: ENGINE INITIALIZED WITH "SAVING BEST MODEL" and Early Stopping MODE\n')
earlystop_callback = tf.keras.callbacks.EarlyStopping(
monitor=bestModelCond['monitor'],
min_delta=bestModelCond['min_delta'],
patience=bestModelCond['patience']
)
if callbacks is None:
callbacks=[earlystop_callback]
else:
callbacks.append(earlystop_callback)

[Security] Workflow deploy-docs.yml is using vulnerable action actions/checkout

The workflow deploy-docs.yml is referencing action actions/checkout using references v1. However this reference is missing the commit a6747255bd19d7a757dbdda8c654a9f84db19839 which may contain fix to the some vulnerability.
The vulnerability fix that is missing by actions version could be related to:
(1) CVE fix
(2) upgrade of vulnerable dependency
(3) fix to secret leak and others.
Please consider to update the reference to the action.

Not installing MedicalAI

pip install medicalai -q is not working now.Please solve this issue.

Predict function might go into infinite loop on some systems

Solution 1:

Try reinstalling medicalai+ TensorFlow in a new and clean virtual environment.

Solution 2: Use the following modifications

For Prediction:

predictions = infEngine.model.predict(testGen.generator,verbose=1, steps=testGen.STEP_SIZE)
predictions = trainer.model.predict(testGen.generator,verbose=1, steps=testGen.STEP_SIZE)

For generate_evaluation_report:

generate_evaluation_report(testSet=testGen, predictions = infEngine.model.predict( testGen.generator, verbose=1, steps=testGen.STEP_SIZE ) )