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arXiv Computer Vision By Chuxi Nan, Di Wu, Hongming Guo, Ning Cao, Xiaohui Zhu, Zhaoting Shi, Jiawei Li

Multimodal Fusion for Fine-Grained Classification of Breast Fibroadenoma and Phyllodes Tumors

Read the original on arXiv Computer Vision →

The paper introduces the FAPT-M Dataset, a pathology‑confirmed multimodal collection of 910 breast patients that includes ultrasound images, structured clinical attributes, and diagnostic descriptions. It proposes a clinically guided multimodal framework that fuses DenseNet visual encoding, CLIP‑inspired text encoding, and lightweight clinical encoding, enhanced by clinical‑conditioned adaptive modulation, cross‑modal Transformer fusion, and dual‑path representation learning. The method achieves 77.64% accuracy, 73.38% F1‑score, and 89.74% AUC, outperforming CNN, Transformer, and vision‑language baselines, and demonstrates the value of three‑modality fusion through ablation and class‑balanced studies.

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 Computer Vision.

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