Tip #1: Augmentation Is King

DNN-based models are data-hungry, but acquiring biological and biomedical data can be costly. Often, you may be faced with a modeling task that has a very small number of samples. My first tip is to try everything you can to make data augmentation work.

For image data, there are well-known techniques for augmenting it, such as shifting, flipping, rotating, and zooming. However, augmenting non-image data can be more challenging. The key is to develop the "right" augmentation scheme based on the characteristics of the specific data you have. For example, for signal trace data, mixing two traces at random ratios may produce some reasonable augmented data. For sequence data, you might try capturing the statistical characteristics of the real data and then generating new sequences based on those characteristics.

To determine whether your augmentation scheme is effective, compare the model performance on unaugmented test data and augmented validation data. If there is a significant difference, it means your augmentation scheme has failed and you should go back to the drawing board and try again.

Data augmentation is not easy and does not always work, but it is worth attempting. Finding a way to create a lot of data from thin air that can significantly improve the performance of your models is very rewarding.

Read next tip: Do Transfer Learning When You Can


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