Search papers, labs, and topics across Lattice.
This study addresses the challenge of accurately classifying breast fibroadenoma (FA) and phyllodes tumors (PT) using a novel multimodal framework that integrates ultrasound imaging, clinical attributes, and textual descriptions. By constructing the FAPT-M Dataset with 910 patients and employing advanced techniques such as clinical-conditioned adaptive modulation and cross-modal Transformer fusion, the authors significantly enhance feature alignment and interaction across modalities. The proposed method achieves a patient-level accuracy of 77.64%, surpassing existing single-modal and multimodal baselines, thereby establishing a new benchmark in breast ultrasound analysis.
Achieving 77.64% accuracy in distinguishing between breast fibroadenoma and phyllodes tumors could revolutionize preoperative decision-making in clinical settings.
Breast fibroadenoma (FA) and phyllodes tumor (PT) are fibroepithelial breast lesions with highly overlapping appearances on B-mode ultrasound, making benign and borderline PT prone to being misclassified as FA and complicating preoperative decision-making. Existing computer-aided diagnosis methods commonly rely on single-modal imaging features and insufficiently exploit complementary clinical and textual information. To address this limitation, we construct the FAPT-M Dataset, a pathology-confirmed multimodal dataset comprising 910 patients with strictly reviewed ultrasound images, structured clinical attributes, and ultrasound diagnostic descriptions. Based on this dataset, we propose a clinically guided multimodal framework that integrates DenseNet-based visual encoding, CLIP-inspired text encoding, and lightweight clinical encoding, and further introduces clinical-conditioned adaptive modulation, cross-modal Transformer fusion, and dual-path representation learning to improve feature alignment and multimodal interaction. Under patient-level five-fold cross-validation, the proposed method achieves an accuracy of 77.64%, F1-score of 73.38%, and AUC of 89.74%, outperforming representative CNN-, Transformer-, and vision-language-based baselines. Ablation studies and class-balanced evaluations further confirm the contribution of three-modality fusion and the key architectural components. Overall, this work provides an effective multimodal approach for fine-grained FA-PT classification and establishes a high-quality benchmark for multimodal breast ultrasound analysis.
