arXiv:2606. 29928v1 Announce Type: cross Abstract: Multimodal Large Models have significantly advanced automated breast ultrasound diagnosis.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2607. 00147v1 Announce Type: new Abstract: Rare disease differential diagnosis is a critical yet arduous clinical task, requiring physicians to identify precise phenotypes from complex, unstructured patient symptoms and execute intricate reasoning within a vast search space.
Breast cancer remains a leading cause of cancer-related mortality among women. Its clinical management requires multimodal reasoning across a clinical workflow that spans \textit{screening}, \textit{diagnosis} and \textit{treatment planning}, where each stage involves distinct imaging modalities, task objectives, and reasoning patterns.
Breast ultrasound (BUS) is widely used for breast cancer diagnosis yet remains operator-dependent. While deep learning shows promise, ensuring diagnostic reliability and interpretability is challengin...
arXiv:2505. 14107v5 Announce Type: replace-cross Abstract: The emergence of groundbreaking large language models capable of performing complex reasoning tasks holds significant promise for addressing various scientific challenges, including those arising in complex clinical scenarios.
arXiv:2606. 06224v1 Announce Type: cross Abstract: Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology.
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.