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R.Y. Ianatchkova
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This paper introduces a diagnostic framework for assessing annotation shift in cross-domain machine learning, with a focus on medical imaging applications. We formally define annotation shift as a change in the conditional distribution of assigned labels given the underlying target state. This distinction separates annotation-related effects from prevalence and acquisition-related shifts, which may produce similar observable patterns.
We develop a framework combining input-distribution diagnostics, label-distribution analysis, and bidirectional cross-domain model evaluation to assess whether observed differences are consistent with annotation shift. The approach is evaluated through controlled synthetic experiments and experiments using osteoarthritis radiographs.
Across both settings, annotation shift produces characteristic directional asymmetries in cross-domain prediction errors that differ from the signatures of prevalence and acquisition shifts. These asymmetries provide a basis for distinguishing annotation shift from other forms of domain shift, enabling more reliable interpretation of cross-domain model failures. ...
We develop a framework combining input-distribution diagnostics, label-distribution analysis, and bidirectional cross-domain model evaluation to assess whether observed differences are consistent with annotation shift. The approach is evaluated through controlled synthetic experiments and experiments using osteoarthritis radiographs.
Across both settings, annotation shift produces characteristic directional asymmetries in cross-domain prediction errors that differ from the signatures of prevalence and acquisition shifts. These asymmetries provide a basis for distinguishing annotation shift from other forms of domain shift, enabling more reliable interpretation of cross-domain model failures. ...
This paper introduces a diagnostic framework for assessing annotation shift in cross-domain machine learning, with a focus on medical imaging applications. We formally define annotation shift as a change in the conditional distribution of assigned labels given the underlying target state. This distinction separates annotation-related effects from prevalence and acquisition-related shifts, which may produce similar observable patterns.
We develop a framework combining input-distribution diagnostics, label-distribution analysis, and bidirectional cross-domain model evaluation to assess whether observed differences are consistent with annotation shift. The approach is evaluated through controlled synthetic experiments and experiments using osteoarthritis radiographs.
Across both settings, annotation shift produces characteristic directional asymmetries in cross-domain prediction errors that differ from the signatures of prevalence and acquisition shifts. These asymmetries provide a basis for distinguishing annotation shift from other forms of domain shift, enabling more reliable interpretation of cross-domain model failures.
We develop a framework combining input-distribution diagnostics, label-distribution analysis, and bidirectional cross-domain model evaluation to assess whether observed differences are consistent with annotation shift. The approach is evaluated through controlled synthetic experiments and experiments using osteoarthritis radiographs.
Across both settings, annotation shift produces characteristic directional asymmetries in cross-domain prediction errors that differ from the signatures of prevalence and acquisition shifts. These asymmetries provide a basis for distinguishing annotation shift from other forms of domain shift, enabling more reliable interpretation of cross-domain model failures.