Deep Learning concepts for Medical Imaging — A shallow overview
U-Net Architechture (src: https://arxiv.org/abs/1505.04597)
In this article I go through a few DL concepts used in Medical Imaging. This is a very limited piece, and depends highly on (can be considered a limited summary of) this paper: [https://arxiv.org/abs/1702.05747]. It is a non-mathematical, non-rigorous treatment, with focus on uses and concepts involved.
I will go through the use of deep learning for
- image classification,
- object detection,
- segmentation,
and provide concise overviews of conceptual ideal per application area.
For a fuller treatment, one must go through the article mentioned.
Let’s jump onto it.
Image Classification
Image or exam classification was one of the first areas in which deep learning contributed majorly to medical image analysis. In exam classification, one typically has one or multiple images (an exam) as input, with a single diagnostic variable as output (e.g., disease present or not).
In such a setting, every diagnostic exam is a sample and dataset sizes are typically small compared to those in computer vision (e.g., hundreds/thousands…
