Benchmark shows that combining data rebalancing with feature disentanglement mitigates shortcut learning more effectively than rebalancing alone in medical imaging models.
The subgroup imperative: chest radiograph classifier generalization gaps in patient, setting, and pathology subgroups.Radiology: Artificial Intelligence, 5(5):e220270, 2023
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Mitigating Shortcut Learning via Feature Disentanglement in Medical Imaging: A Benchmark Study
Benchmark shows that combining data rebalancing with feature disentanglement mitigates shortcut learning more effectively than rebalancing alone in medical imaging models.