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

Fully Convolutional Model for Variable Bit Length and Lossy High Density Compression of Mammograms