A
system and method for an innovative approach to
medical imaging compression and
encryption, specifically designed for
tomosynthesis data. It transforms input data to a dyadic distribution, optimizing it for Huffman encoding while preserving critical
diagnostic information. The compressed data is processed through a Large Codeword Model (LCM) incorporating a latent
transformer, which learns complex patterns and relationships within the
imaging data. This process not only achieves high compression ratios but also provides inherent
encryption. A neural upsampler, trained to invert the dyadic transformation, reconstructs the original image with
high fidelity. The
system is particularly effective for handling the redundancies inherent in
tomosynthesis datasets, where adjacent slices often contain similar information. By balancing compression efficiency with
diagnostic accuracy, this method enables secure storage and transmission of large
medical imaging datasets while maintaining the ability to reconstruct high-quality images for accurate diagnosis.