Hyperparameter optimization in transfer learning for medical image analysis

Rune Johan Borgli

Medical images from gastroenterology

Gastrointestinal (GI) tract prone to many different diseases

Doctors use sensor and image data to diagnose

The procedure's success heavly dependent on doctor

Images from colonoscopies of different conditions

The problem

We use standard Bayesian optimization

Difficult to create large datasets required for deep learning

Transfer learning is a solution

Choosing hyperparameters for fine-tuning is unintuitive

Choosing delimiting layer in fine-tuning is complex and previous work has done this manually

We optimize with regards to validation accuracy

Three ways to structure the optimization

The hyperparameters we optimize are the pre-trained model, optimizer, learning rate and delimiting layer

our approach

Complex structure and very difficult to know which layers to tune

Results from 8-class dataset

Separate optimizations

Separate hyperparameters

Shared hyperparameters

Layer optimization

Validation accuracy

Validation accuracy

Epoch

Time

Model

InceptionResNetV2

Optimizer

Adadelta

Learning Rate

0.84

Layer

136

Conclusion

Automatic hyperparameter optimization with our hyperparameters including delimiting layer is successful in achieving better results

The delimiting layer from the automatic hyperparameter optimization is not trivial to choose manually

Shared hyperparameters gave the best results

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