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English(EN) Unsupervised Domain Adaptation for Calcification Classification in Mammography Across Multi-Site Datasets

AI框架改进了跨数据集的乳腺钼靶钙化分类

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个新框架，以提高AI模型在不同数据集和成像技术中对乳腺钼靶钙化进行分类的准确性。该框架利用无监督域自适应，采用AdaIN和CycleGAN等风格迁移模型生成多样化的训练数据，而无需额外的标注。然后，Swin Transformer V2骨干网络执行分类。该方法在外部分数据集上表现出改进的性能，表明其有潜力减少域偏移并增强乳腺钼靶诊断AI工具的泛化能力。 AI

影响 通过提高跨不同数据集和成像技术的泛化能力，增强了乳腺钼靶的AI诊断能力。

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

在 arXiv cs.CV 阅读 →

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AI框架改进了跨数据集的乳腺钼靶钙化分类

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Xuan Liu, Derek L. Nguyen, Emily C. Barre, Jennifer Thomas, Thomas Lynch, Jeffrey R. Marks, E. Shelley Hwang, Marc D. Ryser, Joseph Y. Lo, Lars J. Grimm ·

    Unsupervised Domain Adaptation for Calcification Classification in Mammography Across Multi-Site Datasets

    arXiv:2607.06549v1 Announce Type: new Abstract: Deep learning-based computer-aided diagnosis (CAD) systems have shown strong performance in breast cancer diagnosis, particularly for classification tasks in mammography. However, domain shifts across multi-site datasets remain a ch…

  2. arXiv cs.CV TIER_1 English(EN) · Lars J. Grimm ·

    多中心数据集乳腺X线摄影钙化分类的无监督域自适应

    Deep learning-based computer-aided diagnosis (CAD) systems have shown strong performance in breast cancer diagnosis, particularly for classification tasks in mammography. However, domain shifts across multi-site datasets remain a challenge, especially when models are applied to u…

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