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.
Screenshot from HBO’s Not Hotdog app developed by “SeeFood Technologies” in the show, Silicon Valley.
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.
How can you effectively train a neural network to classify medical images with limited training data. One answer is to augment the labeled data you already have and feed the transformed images into your model. Augmentation serves two purposes. First, additional labeled training data from augmentation in theory will improve your image classification model accuracy [WARNING!!! can lead to overfitting]. Second, the transformations will allow the model to train on orientation variations. Possibly providing the model flexibility when encountering subtle variation shifts in testing or real world data.
Data Augmentation is a technique in Deep Learning which helps in adding value to our base dataset by adding the gathered information from various sources to improve the quality of data of an organisation.
An implementation with Keras. Whenever you build and train a model for a machine learning task, regardless of its being a classification or regression one.
Data Augmentation is a technique in Deep Learning which helps in adding value to our base dataset by adding the gathered information.
The problem with deep learning models is they need lots of data to train a model. There are two major problems while training deep learning models is overfitting and underfitting of the model. Those problems are solved by data augmentation is a regularization technique that makes slight modifications to the images and used to generate data.
PyTorch for Deep Learning | Data Science | Machine Learning | Python. PyTorch is a library in Python which provides tools to build deep learning models. What python does for programming PyTorch does for deep learning. Python is a very flexible language for programming and just like python, the PyTorch library provides flexible tools for deep learning.