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Data Augmentation in Medical Images
How to improve vision model performance by reshaping and resampling data
The popularization of machine learning has changed our world in wonderful ways. Some notable applications of machine learning allow us to do the previously unthinkable, like determining if an image is a hot dog or not a hot dog.
The ease to develop image recognition and classification applications has been streamlined in the last few years with the release of open source neural network frameworks like TensorFlow and PyTorch. Usage of these neural network frameworks is predicated on the availability of labeled training data, which has become more accessible within cloud infrastructures. Neural networks require large amounts of data to properly weight the functions between layers. However, in fields like medical imaging, large amounts of labeled training data are not always available. For those interested in medical imaging data, a great resource can be found at Giorgos Sfikas’ GitHub. A great resource for a general overview of data augmentation techniques and tools can be found on Neptune.ai.
