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English(EN) Few-Shot Learning Pipeline for Monkeypox Skin Disease Classification Using CNN Feature Extractors

少样本学习管道利用CNN辅助猴痘皮肤病分类

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个少样本学习管道，利用卷积神经网络（CNN）对猴痘及类似皮肤病进行分类。该方法通过采用一种名为SimpleShot的轻量级分类器和预训练的CNN骨干网络，解决了罕见病标记数据有限的挑战。在多个数据集和配置上的实验表明，MobileNetV2_100表现最佳，而跨数据集评估突显了领域鲁棒性对于临床部署的重要性。 AI

影响 展示了一种在数据有限的情况下将AI应用于罕见病分类的实用方法，有可能提高服务不足地区的诊断能力。

排序理由 学术论文，详细介绍了用于医学图像分类的新型少样本学习管道。

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 →

少样本学习管道利用CNN辅助猴痘皮肤病分类

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Md. Safirur Rashid, Sabbir Ahmed, Muhammad Usama Islam, Sumona Hoque Mumu, Md. Hasanul Kabir ·

    Few-Shot Learning Pipeline for Monkeypox Skin Disease Classification Using CNN Feature Extractors

    arXiv:2605.05034v1 Announce Type: new Abstract: Despite the strong performance of Convolutional Neural Networks (CNNs) in disease classification, their effectiveness often depends on access to large annotated datasets, which is an impractical requirement for emerging or rare cond…

  2. arXiv cs.CV TIER_1 English(EN) · Md. Hasanul Kabir ·

    Few-Shot Learning Pipeline for Monkeypox Skin Disease Classification Using CNN Feature Extractors

    Despite the strong performance of Convolutional Neural Networks (CNNs) in disease classification, their effectiveness often depends on access to large annotated datasets, which is an impractical requirement for emerging or rare conditions such as Monkeypox. To overcome this limit…

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