Noise-Aware Visual Representation Learning for Medical Visual Question Answering

Researchers propose a noise-aware medical visual question answering framework that uses denoising autoencoders to improve the robustness of visual representations when connecting vision encoders to large language models. The approach achieves competitive performance on medical imaging benchmarks while demonstrating enhanced resilience to noisy inputs through parameter-efficient fine-tuning.

This research addresses a critical gap in medical AI systems where the integration of vision encoders with language models often fails to account for noise and perturbations in visual data. Medical imaging inherently contains artifacts, compression artifacts, and variations that can degrade model performance, making robustness essential for clinical deployment. The proposed denoising autoencoder acts as an intermediary layer that filters out irrelevant noise while preserving clinically significant information before embeddings reach the language model.

The approach builds on the growing trend of connecting lightweight adapter networks between specialized domain models and general-purpose LLMs. Rather than retraining entire systems, the researchers leverage low-rank adaptation (LoRA) for efficient parameter updates, reducing computational overhead—a practical necessity for healthcare institutions with limited infrastructure. This aligns with broader efforts to democratize advanced AI capabilities across resource-constrained environments.

For the medical AI sector, this work demonstrates that robustness improvements can be achieved without sacrificing performance on clean data. Evaluation on SLAKE and PathVQA benchmarks shows the framework maintains competitive accuracy while improving graceful degradation when faced with corrupted inputs. This matters significantly for real-world deployment where medical imaging systems encounter various quality issues, scanner variations, and preprocessing artifacts.

Looking forward, the integration of noise-awareness into medical AI pipelines could become standard practice. As healthcare institutions increasingly adopt AI-assisted diagnostic tools, the ability to maintain reliability across diverse imaging conditions becomes a regulatory and safety requirement. The modular nature of this approach suggests it could be adapted to other medical imaging tasks and clinical applications.

• →Denoising autoencoders improve visual representation robustness in medical VQA systems by reconstructing clean embeddings from corrupted inputs.
• →The framework maintains competitive performance on clean data while significantly improving robustness to noisy inputs across multiple benchmarks.
• →Parameter-efficient fine-tuning through LoRA enables practical deployment without full model retraining or prohibitive computational costs.
• →Noise-aware design patterns in medical AI address real-world deployment challenges where imaging data quality varies across clinical settings.
• →The modular approach using lightweight adapters suggests applicability beyond VQA to other medical image analysis tasks.