Localization Lens for Improving Medical Vision-Language Models
Medical Vision-Language Models (Med-VLMs) have demonstrated strong capabilities in clinical tasks. However, they often struggle to understand anatomical structures and spatial positioning, which are crucial for medical reasoning. To address this, we propose a localization-aware enhancement to the Med-VLM pipeline, introducing improvements at three levels: data,architecture, and alignment. First, we introduce localization lens, a set of expert-validated representations that provide richer anatomical and positional context. However, as these representations increase input complexity, we integrate pixel shuffle within the model architecture to filter and refine representations, enhancing spa...
| Authors | Hasan Farooq, Murtaza Taj, Mehwish Nasim et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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