Issues with automatic and team-based QC.

<p>When maintaining large neuroimaging datasets with multiple processing pipelines, shallow quality control processes that rely on derived metrics can fail to catch instances of algorithmic failures. However, deep QC processes quickly become unscalable and inefficient as the amount of data ava...

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Main Author:	Michael E. Kim (21956727) (author)
Other Authors:	Chenyu Gao (9501623) (author), Nancy R. Newlin (21956730) (author), Gaurav Rudravaram (21956733) (author), Aravind R. Krishnan (21956736) (author), Karthik Ramadass (13949238) (author), Praitayini Kanakaraj (9193371) (author), Kurt G. Schilling (9193353) (author), Blake E. Dewey (19066193) (author), David A. Bennett (229710) (author), Sid O’Bryant (6449120) (author), Robert C. Barber (21956739) (author), Derek Archer (21956742) (author), Timothy J. Hohman (8356254) (author), Shunxing Bao (21956745) (author), Zhiyuan Li (205620) (author), Bennett A. Landman (7623857) (author), Nazirah Mohd Khairi (21956748) (author)
Published:	2025
Subjects:	Biophysics Cell Biology Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified quantitative outlier detection thorough quality control rater variability experiment several processing pipelines proposed method satisfies low time cost robust visual qc scalable quality control div >< p automated qc method following design criteria data processing pipelines quality data scalable manner design criteria comparison experiment time spent time consuming qc pipeline yet necessary slight differences quick visualization preprocessed data erroneous conclusions easily shared also provide algorithm failure 3 .) 2 .) 1 .)
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Issues with automatic and team-based QC.

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