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Abstract #0222

Subtle Inverse Crimes: Naively using Publicly Available Images Could Make Reconstruction Results Seem Misleadingly Better!

Efrat Shimron1, Jonathan Tamir2, Ke Wang1, and Michael Lustig1
1Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley, CA, United States, 2Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States

This work reveals how naively using publicly available data for training and evaluating reconstruction algorithms may lead to artificially improved algorithm performance. We observed such practice in the “wild” and aim to bring this to the attention of the community. The underlying cause is common data preprocessing pipelines which are often ignored: k-space zero-padding in clinical scanners and JPEG compression in database storage. We show that retrospective subsampling of such preprocessed data leads to overly-optimistic reconstructions. We demonstrate this phenomenon for Compressed-Sensing, Dictionary-Learning and Deep Neural Networks. This work hence highlights the importance of careful task-adequate usage of public databases.

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