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Training certified detectives to track down the intrinsic shortcuts in COVID-19 chest x-ray data sets (preprint)
researchsquare; 2023.
Preprint
in English
| PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-2818347.v1
ABSTRACT
Deep learning faces a significant challenge wherein the trained models often underperform when used with external test data sets. This issue has been attributed to spurious correlations between irrelevant features in the input data and corresponding labels. This study uses the classification of COVID-19 from chest x-ray radiographs as an example to demonstrate that the image contrast and sharpness, which are characteristics of a chest radiograph dependent on data acquisition systems and imaging parameters, can be intrinsic shortcuts that impair the model’s generalizability. The study proposes training certified shortcut detective models that meet a set of qualification criteria which can then identify these intrinsic shortcuts in a curated data set.
Full text:
Available
Collection:
Preprints
Database:
PREPRINT-RESEARCHSQUARE
Main subject:
COVID-19
/
Mixed Connective Tissue Disease
Language:
English
Year:
2023
Document Type:
Preprint
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