Location-Aware Pretraining for Medical Difference Visual Question Answering
Differential medical VQA models compare multiple images to identify clinically meaningful changes and rely on vision encoders to capture fine-grained visual differences that reflect radiologists' comparative diagnostic workflows. However, vision encoders trained using standard contrastive or classification objectives often fail to capture the subtle variations needed to distinguish true disease progression from acquisition-related variability. To address this limitation, we introduce a location-aware pretraining framework that incorporates automatic referring expressions (AREF), grounded captioning (GCAP), and conditional automatic referring expressions (CAREF). These tasks promote the le...
| Authors | Denis Musinguzi, Caren Han, Prasenjit Mitra |
|---|---|
| Primary category | cs.CV |
| Published | 5 March 2026 |
| Last updated at source | 22 April 2026 |
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