arXiv Computer Vision August 26, 2026 By Ming Cheng, Hongyu Sun, Zhaolin Chen, Jun Liu, Hossein Rahmani, Qiuhong Ke

Boot-and-Feedback Framework for Generalist-Expert Model Collaboration in Breast Ultrasound Diagnosis

Read the original on arXiv Computer Vision →

The paper introduces the Boot-and-Feedback (BooF) framework, which facilitates collaboration between multimodal large language models (MLLMs) and expert vision models for breast ultrasound (BUS) diagnosis. In the Boot Stage, the MLLM is guided by the BI-RADS lexicon and preliminary expert predictions to generate reliable textual descriptions, while the Feedback Stage fuses these descriptions with visual features using an Attention-Gated Cross-Modality Fusion Module, allowing the expert model to incorporate textual insights and filter out noise. Experiments on multiple BUS datasets show that BooF improves both diagnostic accuracy and interpretability compared to existing methods.

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