Massive collection and explosive growth of the huge amount of medical data, demands effective compression for efficient storage, transmission and sharing. Readily available visual data compression techniques have been studied extensively but tailored for nature images/videos, and thus show limited performance on medical data which are of different characteristics. Emerging implicit neural representation (INR) is gaining momentum and demonstrates high promise for fitting diverse visual data in target-data-specific manner, but a general compression scheme covering diverse medical data is so far absent. To address this issue, we firstly derive a mathematical explanation for INR's spectrum concentration property and an analytical insight on the design of compression-oriented INR architecture. Further, we design a funnel shaped neural network capable of covering broad spectrum of complex medical data and achieving high compression ratio. Based on this design, we conduct compression via optimization under given budget and propose an adaptive compression approach SCI, which adaptively partitions the target data into blocks matching the concentrated spectrum envelop of the adopted INR, and allocates parameter with high representation accuracy under given compression ratio. The experiments show SCI's superior performance over conventional techniques and wide applicability across diverse medical data.
翻译:大量医疗数据的大规模收集和爆炸性增长,要求有效地压缩大量医疗数据,以便有效地储存、传输和共享。 现成的视觉数据压缩技术已经进行了广泛研究,但专门为自然图像/视频作了专门设计,因此在具有不同特点的医疗数据方面表现有限。 新兴的隐性神经代表(INR)正在形成势头,并显示出以特定目标数据方式适当提供各种视觉数据的高度希望,但迄今还没有一个涵盖各种医疗数据的一般压缩计划。为了解决这个问题,我们首先从数学角度解释IRR的频谱集中特性,并对面向压缩的IRR结构的设计进行分析性的洞察。 此外,我们设计了一个能覆盖广泛复杂医疗数据并实现高压缩率的漏斗型神经网络。基于这一设计,我们通过在特定预算下优化进行压缩,并提议一个适应性压缩方法,将目标数据隔离在与采纳的IRR的集中频谱孔相匹配的区块中,并根据压缩率分配具有高代表性的参数。实验显示SCI公司在常规技术方面的优异性以及广泛适用于各种医疗数据。