GRASP-PsONet: Gradient-based Removal of Spurious Patterns for PsOriasis Severity Classification
Journal:
arXiv
Published Date:
Jun 27, 2025
Abstract
Psoriasis (PsO) severity scoring is important for clinical trials but is
hindered by inter-rater variability and the burden of in person clinical
evaluation. Remote imaging using patient captured mobile photos offers
scalability but introduces challenges, such as variation in lighting,
background, and device quality that are often imperceptible to humans but can
impact model performance. These factors, along with inconsistencies in
dermatologist annotations, reduce the reliability of automated severity
scoring. We propose a framework to automatically flag problematic training
images that introduce spurious correlations which degrade model generalization,
using a gradient based interpretability approach. By tracing the gradients of
misclassified validation images, we detect training samples where model errors
align with inconsistently rated examples or are affected by subtle, nonclinical
artifacts. We apply this method to a ConvNeXT based weakly supervised model
designed to classify PsO severity from phone images. Removing 8.2% of flagged
images improves model AUC-ROC by 5% (85% to 90%) on a held out test set.
Commonly, multiple annotators and an adjudication process ensure annotation
accuracy, which is expensive and time consuming. Our method detects training
images with annotation inconsistencies, potentially removing the need for
manual review. When applied to a subset of training data rated by two
dermatologists, the method identifies over 90% of cases with inter-rater
disagreement by reviewing only the top 30% of samples. This improves automated
scoring for remote assessments, ensuring robustness despite data collection
variability.
