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English(EN) Multimodal Skin Lesion Classification with Swin Transformer and Clinical Metadata Fusion

Swin Transformer和临床数据提升皮肤病变分类准确性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的多模态皮肤病变分类框架，旨在提高皮肤癌的早期诊断率。该方法将Swin Transformer提取的视觉特征与结构化临床元数据相结合。在公开数据集上的实验显示，测试准确率为92.55%，宏观F1分数达到91.33%，即使在少数类上表现也强劲。该模型还结合了温度缩放以进行校准和不确定性估计，以增强预测的可靠性和置信度。 AI

影响 提高皮肤癌检测的诊断准确性，可能有助于早期诊断和治疗。

排序理由 详细介绍新模型架构和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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Swin Transformer和临床数据提升皮肤病变分类准确性

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详细介绍新模型架构和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Nethmi Pathirana, Isuru Munasinghe, Dileeka Alwis ·

    基于Swin Transformer和临床元数据融合的多模态皮肤病变分类

    arXiv:2608.07574v1 Announce Type: new Abstract: Skin lesion classification plays an important role in supporting the early diagnosis of skin cancer. However, automated analysis remains challenging due to class imbalance, inter-class similarity, and intra-class variability in derm…

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