Why chose CNN for a variant caller Google released their variant caller DeepVariant which won the highest SNP performance award in the Precision FDA Truth challenge (99.999% accuracy).

From the linked github repo, we see that DeepVariant is a CNN, we provide images of the reads as an input and train the neural network with a stochastic gradient descent algorithm. 

To me this is unintuitive. Why use images of reads when you already have the reads data?
It's more "images of reads" rather than actual images of reads. In reality, they're feeding in a pileup, which is just a matrix of letters or numbers (you can visualize this as an image, of course). This is more apparent if you look at one of their example python notebooks, CNNs are popular in the image processing field, but at the end of the day images are just large matrices of values so any time you can convert your input into that sort of format then a CNN can be useful (provided you have sufficient training data and some GPUs to throw at things).