Deep Learning in Data Limited Medical Imaging Scenarios
In recent years, deep neural networks have improved the benchmarks of learning in areas such as vision and speech recognition. This improvement comes with a big price tag. Deep neural networks are very large supervised models and need huge quantities of labelled data at the time of training. In medical image analysis, labeling data is expensive. In certain imaging modalities, such as MRI and CT, 3D analysis and segmentation are required which increases the size of networks and limit our ability to use transfer learning from 2D models of the mainstream computer vision community.