Huge streams of diagnostic images are expected to be produced daily in the emerging field of digital microbiology imaging because of the ongoing worldwide spread of Full Laboratory Automation systems. This is redefining the way microbiologists execute diagnostic tasks. In this context, the authors want to assess the suitability and effectiveness of a deep learning approach to solve the diagnostically relevant but visually challenging task of directly identifying pathogens on bacterial growing plates. In particular, starting from hyperspectral acquisitions in the VNIR range and spatial-spectral processing of cultured plates, they approach the identification problem as the classification of computed spectral signatures of the bacterial colonies. In a highly relevant clinical context (urinary tract infections) and on a database of acquired hyperspectral images, they designed and trained a convolutional neural network for pathogen identification, assessing its performance and comparing it against conventional classification solutions. At the same time, given the expected data flow and possible conservation and transmission needs, they are interested in evaluating the combined use of classification and lossy data compression. To this end, after selecting a suitable wavelet-based compression technology, they test coding strength-driven operating points looking for configurations able to provably prevent any classification performance degradation.

In this work we compare state-of-the-art 3D coding technologies which are suitable for compression of 3D (volumetric) medical imagery. Our aim is to assess the usage of these methods on representative large datasets, also keeping into account relevant features related to modern application requirements. This work wants to be a useful reference for those interested in medical image coding technologies evaluation and, more in general, in the interdisciplinary debate about the usage of irreversible compression technologies for an improved and cost effective handling of diagnostic imaging processes and infrastructures (PACS, teleradiology) involving large datasets. Reproducibility and possible extension of this study are guaranteed by the use of publicly available reference software and datasets

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