Abstract
The growth of data throughput in optical microscopy has triggered the extensive use of supervised learning (SL) models on compressed datasets for automated analysis. Investigating the effects of image compression on SL predictions is therefore pivotal to assess their reliability, especially for clinical use. We quantify the statistical distortions induced by compression through the comparison of predictions on compressed data to the raw predictive uncertainty, numerically estimated from the raw noise statistics measured via sensor calibration. Predictions on cell segmentation parameters are altered by up to 15% and more than 10 standard deviations after 16-to-8 bits pixel depth reduction and 10:1 JPEG compression. JPEG formats with higher compression ratios show significantly larger distortions. Interestingly, a recent metrologically accurate algorithm, offering up to 10:1 compression ratio, provides a prediction spread equivalent to that stemming from raw noise. The method described here allows to set a lower bound to the predictive uncertainty of a SL task and can be generalized to determine the statistical distortions originated from a variety of processing pipelines in AI-assisted fields.
Highlights
The growth of data throughput in optical microscopy has triggered the extensive use of supervised learning (SL) models on compressed datasets for automated analysis
We show the statistical nature of the distortions induced by image compression on the outcomes of SL tasks in optical microscopy
We show that 16-to-8 bits pixel depth reduction and JPEG compression can alter SL outcomes by more than 10 standard deviations
Summary
The growth of data throughput in optical microscopy has triggered the extensive use of supervised learning (SL) models on compressed datasets for automated analysis. Image compression can modify SL predictions with respect to when raw datasets are used and lead to unreliable scientific outcomes, based on how much the statistical distribution of the final predictions is altered. For this reason, the statistical distortions induced by compression need to be quantified to investigate the tolerability of image compression methods for SL applications. The statistical distortions induced by compression need to be quantified to investigate the tolerability of image compression methods for SL applications To this end, it is crucial to measure the statistical distribution of the SL outcomes in the absence of compression, in other terms the prediction uncertainty associated to raw data. Investigating the representational uncertainty of the AI pipeline, consisting in errors due to the data representation adopted for training or testing the SL model
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