  Early works on medical image compression date to the 1980's with the impetus
on deployment of teleradiology systems for high-resolution digital X-ray
detectors. Commercially deployed systems during the period could compress 4,096
x 4,096 sized images at 12 bpp to 2 bpp using lossless arithmetic coding, and
over the years JPEG and JPEG2000 were imbibed reaching upto 0.1 bpp. Inspired
by the reprise of deep learning based compression for natural images over the
last two years, we propose a fully convolutional autoencoder for diagnostically
relevant feature preserving lossy compression. This is followed by leveraging
arithmetic coding for encapsulating high redundancy of features for further
high-density code packing leading to variable bit length. We demonstrate
performance on two different publicly available digital mammography datasets
using peak signal-to-noise ratio (pSNR), structural similarity (SSIM) index and
domain adaptability tests between datasets. At high density compression factors
of >300x (~0.04 bpp), our approach rivals JPEG and JPEG2000 as evaluated
through a Radiologist's visual Turing test.
