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English(EN) Multimodal Fusion for Fine-Grained Classification of Breast Fibroadenoma and Phyllodes Tumors

新多模态AI框架提高了乳腺肿瘤分类的准确性

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新的多模态框架，用于对乳腺纤维腺瘤和叶状肿瘤进行分类，这两种肿瘤在超声图像上常常具有相似的外观。该框架使用DenseNet、受CLIP启发的文本编码和Transformer融合来整合视觉、文本和临床数据。所提出的方法在新构建的FAPT-M数据集上达到了77.64%的准确率和89.74%的AUC，优于现有的基线方法。 AI

影响 这种多模态方法可以提高对复杂医学状况的诊断准确性，从而可能改善患者的治疗效果。

排序理由 该集群包含一篇详细介绍用于医学图像分类的新多模态AI框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新多模态AI框架提高了乳腺肿瘤分类的准确性

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · 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

    arXiv:2607.02091v1 Announce Type: new Abstract: 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 preop…

  2. arXiv cs.CV TIER_1 English(EN) · Jiawei Li ·

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

    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…

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