Evi-Steer: Learning to Steer Biomedical Vision-Language Models through Efficient and Generalizable Evidential Tuning
arXiv:2605.26292v1 Announce Type: cross Abstract: Parameter-efficient adaptation of vision-language foundation models is crucial for precise multimodal understanding of biomedical images, yet existing methods remain deterministic and often struggle under domain shift or ambiguous image-text alignment. This limitation is particularly critical in the clinic, where models should remain robust in low-data regimes and domain shifts. We present Evi-Steer, an evidential cross-modal low-dimensional steering framework for BiomedCLIP that enables uncertainty-aware parameter-efficient fine-tuning while u
The increasing sophistication of AI models and the critical need for reliable AI in fields like biomedicine are driving continuous innovation in making these models more robust and adaptable.
Biomedical vision-language models require high accuracy and robustness due to critical applications in healthcare, where errors can have severe consequences, making reliable adaptation methods crucial.
This research introduces a novel method for more robust and uncertainty-aware fine-tuning of biomedical vision-language models, improving their performance in low-data and domain-shifted environments.
- · Medical AI developers
- · Biomedical research institutions
- · Healthcare providers
- · Patients
- · Developers relying on deterministic, less adaptable AI fine-tuning methods
Improved diagnostic accuracy and drug discovery processes from more reliable AI models.
Accelerated development of AI-driven personalized medicine and preventative care solutions.
Enhanced trust in AI systems within critical sectors, fostering broader adoption and integration into decision-making processes.
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